Systems, methods, kits, and apparatuses for specialized chips for robotic intelligence layers

Abstract

A system may include a robotic control circuit configured to control one or more robotic functions of a robot. A system may include a plurality of sensors configured to collect data. A system may include a governance analysis circuit configured to analyze the data and select one or more governance frameworks based on the analyzed data. A system may include a governance model circuit configured to generate a model that applies the one or more governance frameworks to determine one or more governance actions, wherein the robotic control circuit is configured to control the one or more robotic functions in accordance with the one or more governance actions, wherein the robotic control circuit, the governance analysis circuit, and the governance model circuit are integrated on a single substrate.

Description

SYSTEMS, METHODS, KITS, AND APPARATUSES FOR SPECIALIZED CHIPS FOR ROBOTIC INTELLIGENCE LAYERSCROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims the benefit of:U.S. Provisional Application No. 63 / 535,748, filed on 31 August 2023, U.S. Provisional Application No. 63 / 536,171, filed on 1 September 2023, U.S. Provisional Application No. 63 / 610,894, filed 15 December 2023, U.S. Provisional Application No. 63 / 621,549, filed 16 January 2024, U.S. Provisional Application No. 63 / 625,597, filed 26 January 2024, and U.S. Provisional Application No. 63 / 638,590, filed 25 April 2024.

[0002] All the foregoing applications are hereby incorporated by reference as if fully set forth herein in their entirety.BACKGROUND

[0003] Traditional robotic systems have faced numerous challenges in meeting the demands of modern applications. These systems often struggle with real-time processing and decisionmaking in complex, dynamic environments, limiting their ability to adapt and respond effectively. The computational capabilities of conventional robotic control systems are frequently insufficient to handle the intricate tasks and vast amounts of sensor data required for advanced robotic operations. Moreover, traditional systems often lack the necessary integration between various components, such as control circuits, data collection, and artificial intelligence models, leading to inefficiencies and reduced performance. The inability to process and analyze large volumes of data in real-time has hindered the development of truly autonomous and intelligent robotic systems. Additionally, conventional robotic systems have faced difficulties in managing and coordinating large fleets of robots, as well as in implementing sophisticated on- device training and adaptation mechanisms. These limitations have ultimately restricted the potential applications and effectiveness of robotic systems across various industries and use cases.SUMMARY

[0004] In some aspects, the techniques described herein relate to a robotic system including: a robotic control circuit configured to control one or more robotic functions of a robot; a plurality of sensors configured to collect data; a governance analysis circuit configured to analyze the data and select one or more governance frameworks based on the analyzed data; and a governance model circuit configured to generate a model that applies the one or more governance frameworks to determine one or more governance actions, wherein the robotic control circuit is configured to control the one or more robotic functions in accordance with theone or more governance actions; wherein the robotic control circuit, the governance analysis circuit, and the governance model circuit are integrated on a single substrate.

[0005] In some aspects, the techniques described herein relate to a robotic system, wherein the one or more governance frameworks include at least one of: safety standards, security standards, quality standards, regulatory standards, or financial standards.

[0006] In some aspects, the techniques described herein relate to a robotic system, wherein the analyzed data indicates a state of an environment containing the robotic system.

[0007] In some aspects, the techniques described herein relate to a robotic system, wherein the one or more governance frameworks include a plurality of governance frameworks, wherein the governance model circuit is further configured to: prioritize the plurality of governance frameworks; and resolve a conflict between a first governance framework of the plurality of governance frameworks and a second governance framework of the plurality of governance frameworks based on respective priorities of the first governance framework and the second governance framework.

[0008] In some aspects, the techniques described herein relate to a robotic system, wherein the governance model circuit is further configured to apply the generated model to a second set of data captured after the generation of the model to determine the one or more governance actions.

[0009] In some aspects, the techniques described herein relate to a robotic system, wherein the governance model circuit is configured to continually adjust the model based on real-time data captured from the plurality of sensors.

[0010] In some aspects, the techniques described herein relate to a robotic system, wherein the robotic control circuit is further configured to control one or more functions of a second robotic system in accordance with the one or more governance actions.

[0011] In some aspects, the techniques described herein relate to a robotic system, wherein the robotic system is further configured to transmit an instruction to the second robotic system to control the one or more functions of the second robotic system.

[0012] In some aspects, the techniques described herein relate to a robotic system, wherein the one or more governance actions include changing a state of the robotic system.

[0013] In some aspects, the techniques described herein relate to a robotic system, wherein the one or more governance actions include changing a task assigned to the robotic system.

[0014] In some aspects, the techniques described herein relate to a robotic system, wherein the one or more governance actions include transmitting a warning or alarm.

[0015] In some aspects, the techniques described herein relate to a robotic system, wherein the one or more governance actions include transforming data to comply with the one or more governance frameworks.

[0016] In some aspects, the techniques described herein relate to a robotic system, wherein the governance model circuit is configured to simulate the one or more governance actions within a digital twin environment.

[0017] In some aspects, the techniques described herein relate to a robotic system, wherein one or more of the robotic control circuit, the governance analysis circuit, and / or the governance model circuit are implemented using specialized Al chips.

[0018] In some aspects, the techniques described herein relate to a robotic system, wherein one or more of the robotic control circuit, the governance analysis circuit, or the governance model circuit are implemented using a combination of CPUs and GPUs.

[0019] In some aspects, the techniques described herein relate to a robotic system, wherein one or more of the robotic control circuit, the governance analysis circuit, or the governance model circuit are configured to use dynamic voltage and frequency scaling.

[0020] In some aspects, the techniques described herein relate to a robotic system, wherein the single substrate includes a 2.5D or 3D stack of chips.

[0021] In some aspects, the techniques described herein relate to a robotic system, wherein one or more of the robotic control circuit, the governance analysis circuit, or the governance model circuit are connected using a high-speed bridge.

[0022] In some aspects, the techniques described herein relate to a robotic system, wherein one or more of the robotic control circuit, the governance analysis circuit, or the governance model circuit are connected to high bandwidth memory.

[0023] In some aspects, the techniques described herein relate to a robotic system, wherein one or more of the robotic control circuit, the governance analysis circuit, and / or the governance model circuit are modular elements connected using die-to-die connectivity.

[0024] In some aspects, the techniques described herein relate to a robotic system including: a robotic control circuit configured to control one or more robotic functions of a robot; a plurality of sensors configured to collect data; a predictive modeling circuit configured to use one or more artificial intelligence models to generate a prediction based on the data; and a predictive model optimization circuit configured to re-train a predictive model of the one or more artificial intelligence models based on one or more conditions detected after generating the prediction, wherein the robotic control circuit, the predictive modeling circuit, and the predictive model optimization circuit are integrated on a single substrate.

[0025] In some aspects, the techniques described herein relate to a robotic system, wherein the predictive model optimization circuit is further configured to train the predictive model based on training data generated by the robotic system.

[0026] In some aspects, the techniques described herein relate to a robotic system, wherein the training data generated by the robotic system includes classification data generated based on the data captured by the plurality of sensors.

[0027] In some aspects, the techniques described herein relate to a robotic system, wherein the predictive model optimization circuit is further configured to train the predictive model based on training data generated by an environment digital twin.

[0028] In some aspects, the techniques described herein relate to a robotic system, wherein the predictive model optimization circuit is configured to re-train the predictive model based on an accuracy of the prediction generated by the predictive modeling circuit.

[0029] In some aspects, the techniques described herein relate to a robotic system, further including a recommendation circuit configured to provide a recommended action for the robotic system based on the prediction.

[0030] In some aspects, the techniques described herein relate to a robotic system, wherein the robotic control system is configured to control the one or more robotic functions of the robot based on the recommended action.

[0031] In some aspects, the techniques described herein relate to a robotic system, wherein the recommended action includes an action and an entity on which the action will be taken.

[0032] In some aspects, the techniques described herein relate to a robotic system, wherein the recommended action further includes a modifier for the action.

[0033] In some aspects, the techniques described herein relate to a robotic system, wherein the robotic system is further configured to simulate the recommended action using an environment digital twin.

[0034] In some aspects, the techniques described herein relate to a robotic system, wherein the recommendation circuit is further configured to provide a second recommended action for controlling one or more robotic functions of a second robotic system.

[0035] In some aspects, the techniques described herein relate to a robotic system, wherein the robotic system is further configured to transmit the second recommended action to the second robotic system.

[0036] In some aspects, the techniques described herein relate to a robotic system, wherein the robotic system is further configured to generate a report indicating an outcome of the recommended action.

[0037] In some aspects, the techniques described herein relate to a robotic system, wherein one or more of the robotic control circuit, the predictive modeling circuit, or the predictive model optimization circuit are implemented using specialized Al chips.

[0038] In some aspects, the techniques described herein relate to a robotic system, wherein one or more of the robotic control circuit, the predictive modeling circuit, or the predictive model optimization circuit are implemented using a combination of CPUs and GPUs.

[0039] In some aspects, the techniques described herein relate to a robotic system, wherein the one or more of the robotic control circuit, the predictive modeling circuit, or the predictive model optimization circuit are configured to use dynamic voltage and frequency scaling.

[0040] In some aspects, the techniques described herein relate to a robotic system, wherein the single substrate includes a 2.5d or 3d stack of chips.

[0041] In some aspects, the techniques described herein relate to a robotic system, wherein one or more of the robotic control circuit, the predictive modeling circuit, or the predictive model optimization circuit are connected using a high-speed bridge.

[0042] In some aspects, the techniques described herein relate to a robotic system, wherein one or more of the robotic control circuit, the predictive modeling circuit, or the predictive model optimization circuit are connected to high bandwidth memory.

[0043] In some aspects, the techniques described herein relate to a robotic system, wherein one or more of the robotic control circuit, the predictive modeling circuit, or the predictive model optimization circuit are modular elements connected using die-to-die connectivity.

[0044] In some aspects, the techniques described herein relate to a robotic system including: a robotic control circuit configured to control one or more robotic functions of a robot; a plurality of sensors configured to collect data; a network interface circuit configured to communicate with other robotic systems via a network; a network analysis circuit configured to use one or more artificial intelligence models to analyze the data and the communication with other robotic systems; and a network optimization circuit configured to optimize the communication with other robotic systems based on the analysis by the network analysis circuit, wherein the robotic control circuit, the network interface circuit, the network analysis circuit, and the network optimization circuit are integrated on a single substrate.

[0045] In some aspects, the techniques described herein relate to a robotic system, wherein the data includes at least one of: a physical signal measurement, network traffic, network device information, or network configuration data.

[0046] In some aspects, the techniques described herein relate to a robotic system, wherein the network analysis circuit is configured to predict a future network condition.

[0047] In some aspects, the techniques described herein relate to a robotic system, wherein the network optimization circuit is configured to optimize one or more of traffic flows between robotic systems on the network, data prioritization on the network, or protocols used by the robotic systems on the network.

[0048] In some aspects, the techniques described herein relate to a robotic system, wherein the network analysis circuit is further configured to generate or update a network digital twin based on the analysis performed by the network analysis circuit.

[0049] In some aspects, the techniques described herein relate to a robotic system, wherein the network optimization circuit is configured to simulate the optimization using the network digital twin.

[0050] In some aspects, the techniques described herein relate to a robotic system, wherein the optimization performed by the network optimization circuit includes optimizing a schedule of the network, a quality of data transmitted between robotic systems via the network, or a security of data transmitted between robotic systems via the network.

[0051] In some aspects, the techniques described herein relate to a robotic system, wherein the optimization performed by the network optimization circuit includes instructing a robotic system to power up or down, switch networks, adjust a transmission schedule, adjust a communication protocol, re-route traffic, or perform compression on data.

[0052] In some aspects, the techniques described herein relate to a robotic system, wherein the optimization performed by the network optimization circuit includes compressing, decompressing, up-sampling, down-sampling, reformatting, delaying, buffering, or rescheduling traffic transmitted to or from robotic systems via the network interface circuit.

[0053] In some aspects, the techniques described herein relate to a robotic system, wherein the optimization performed by the network optimization circuit includes modifying an instruction being routed to a robotic system via the network.

[0054] In some aspects, the techniques described herein relate to a robotic system, wherein the optimization performed by the network optimization circuit includes changing a topology of the network.

[0055] In some aspects, the techniques described herein relate to a robotic system, wherein the optimization performed by the network optimization circuit includes changing a header of a data packet being routed to a robotic system via the network.

[0056] In some aspects, the techniques described herein relate to a robotic system, further including a governance circuit configured to monitor and apply governance actions to traffic transmitted between robotic systems via the network.

[0057] In some aspects, the techniques described herein relate to a robotic system, wherein one or more of the robotic control circuit, the network interface circuit, the network analysis circuit, or the network optimization circuit are implemented using specialized Al chips.

[0058] In some aspects, the techniques described herein relate to a robotic system, wherein one or more of the robotic control circuit, the network interface circuit, the network analysis circuit, or the network optimization circuit are implemented using a combination of CPUs and GPUs.

[0059] In some aspects, the techniques described herein relate to a robotic system, wherein one or more of the robotic control circuit, the network interface circuit, the network analysis circuit, or the network optimization circuit are configured to use dynamic voltage and frequency scaling.

[0060] In some aspects, the techniques described herein relate to a robotic system, wherein the single substrate includes a 2.5d or 3d stack of chips.

[0061] In some aspects, the techniques described herein relate to a robotic system, wherein one or more of the robotic control circuit, the network interface circuit, the network analysis circuit, or the network optimization circuit are connected using a high-speed bridge.

[0062] In some aspects, the techniques described herein relate to a robotic system, wherein one or more of the robotic control circuit, the network interface circuit, the network analysis circuit, or the network optimization circuit are connected to high bandwidth memory.

[0063] In some aspects, the techniques described herein relate to a robotic system, wherein one or more of the robotic control circuit, the network interface circuit, the network analysis circuit, or the network optimization circuit are modular elements connected using die-to-die connectivity.

[0064] In some aspects, the techniques described herein relate to an integrated chipset, including: a robotic control circuit configured to control a set of functions for a set of robots; a data collection circuit configured to handle data from a set of loT devices; a neural network classifier configured to process the data from the loT devices to output a classification of a state of an environment wherein the robots operate; and a neural network control circuit configured to operate on the output of the neural network classifier to output an operational control parameter to the robotic control circuit, wherein the robotic control circuit, the data collection circuit, theneural network classifier and the neural network control circuit share a set of I / O capabilities of the integrated chipset.

[0065] In some aspects, the techniques described herein relate to an integrated chipset, wherein the shared set of I / O capabilities include shared I / O ports.

[0066] In some aspects, the techniques described herein relate to an integrated chipset, wherein the shared set of I / O capabilities include shared data.

[0067] In some aspects, the techniques described herein relate to an integrated chipset, wherein the shared set of I / O capabilities include shared sensors.

[0068] In some aspects, the techniques described herein relate to an integrated chipset, wherein the shared set of I / O capabilities include shared actuators.

[0069] In some aspects, the techniques described herein relate to an integrated chipset, wherein the shared set of I / O capabilities include the set of functions for the set of robots.

[0070] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network classifier includes an application-specific integrated circuit (ASIC).

[0071] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network classifier includes a graphics processing unit (GPU) or tensor processing unit (TPU).

[0072] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network classifier includes an FPGA.

[0073] In some aspects, the techniques described herein relate to an integrated chipset, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a robot that manages the plurality of robots.

[0074] In some aspects, the techniques described herein relate to an integrated chipset, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a platform that manages the plurality of robots.

[0075] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to recognize objects within the environment.

[0076] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to perform scene understanding.

[0077] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to determine an activity of a human within the environment.

[0078] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to recognize sounds in the environment.

[0079] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to determine a pose of at least one robot of the set of robots.

[0080] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to determine a map of the environment.

[0081] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network control circuit is configured to control navigation within the environment.

[0082] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network control circuit is configured to control interactions with objects in the environment.

[0083] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network control circuit is configured to control interactions with humans in the environment.

[0084] In some aspects, the techniques described herein relate to an integrated chipset, including: a robotic control circuit configured to control a set of functions for a set of robots; a data collection circuit configured to handle data from a set of loT devices; a neural network classifier configured to process the data from the loT devices to output a classification of a state of an environment wherein the robots operate; and a neural network control circuit configured to operate on the output of the neural network classifier to output an operational control parameter to the robotic control circuit, wherein the robotic control circuit, the data collection circuit, the neural network classifier and the neural network control circuit are integrated on a common substrate

[0085] In some aspects, the techniques described herein relate to an integrated chipset, wherein the robotic control circuit, the data collection circuit, the neural network classifier, and the neural network control circuit are manufactured on a single silicon wafer, wherein the common substrate is the single silicon wafer.

[0086] In some aspects, the techniques described herein relate to an integrated chipset, wherein the common substrate is a single chip.

[0087] In some aspects, the techniques described herein relate to an integrated chipset, wherein the robotic control circuit, the data collection circuit, the neural network classifier, and the neural network control circuit are separately manufactured chips that are bonded to the common substrate.

[0088] In some aspects, the techniques described herein relate to an integrated chipset, wherein the common substrate is a package, wherein the robotic control circuit, the data collection circuit, the neural network classifier, and the neural network control circuit are enclosed in the package.

[0089] In some aspects, the techniques described herein relate to an integrated chipset, wherein the package encloses a plurality of packages, wherein the plurality of packages are connected via a common interface.

[0090] In some aspects, the techniques described herein relate to an integrated chipset, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a robot that manages the plurality of robots.

[0091] In some aspects, the techniques described herein relate to an integrated chipset, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a platform that manages the plurality of robots.

[0092] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to recognize objects within the environment.

[0093] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to perform scene understanding.

[0094] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to determine an activity of a human within the environment.

[0095] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to recognize sounds in the environment.

[0096] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to determine a pose of at least one robot of the set of robots.

[0097] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to determine a map of the environment.

[0098] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network control circuit is configured to control navigation within the environment.

[0099] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network control circuit is configured to control interactions with objects in the environment.

[0100] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network control circuit is configured to control interactions with humans in the environment.

[0101] In some aspects, the techniques described herein relate to a method performed by an integrated chipset, the method including: controlling, by a robotic control circuit of the integrated chipset, a set of functions for a set of robots; collecting, by a data collection circuit of the integrated chipset, data from a set of loT devices; processing, by a neural network classifier of the integrated chipset, the data from the loT devices to classify a state of an environment wherein the robots operate; and processing, by a neural network control circuit of the integrated chipset, an output of the neural network classifier to output an operational control parameter to the robotic control circuit, wherein the robotic control circuit, the data collection circuit, the neural network classifier and the neural network control circuit are integrated on a common substrate

[0102] In some aspects, the techniques described herein relate to a method, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a robot that manages the plurality of robots.

[0103] In some aspects, the techniques described herein relate to a method, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a platform that manages the plurality of robots.

[0104] In some aspects, the techniques described herein relate to an integrated chipset, including: a robotic control circuit configured to control a set of functions for a set of robots; adata collection circuit configured to handle data from a set of loT devices; a neural network classifier configured to process the data from the loT devices to output a classification of a state of an environment wherein the robots operate; and a neural network control circuit configured to operate on the output of the neural network classifier circuit to output an operational control parameter to the robotic control circuit, wherein the robotic control circuit, the data collection circuit, the neural network classifier, and the neural network control circuit are configured as layers in a 3D chipset architecture.

[0105] In some aspects, the techniques described herein relate to an integrated chipset, wherein the robotic control circuit, the data collection circuit, the neural network classifier, and the neural network control circuit are connected vertically using through-silicon vias.

[0106] In some aspects, the techniques described herein relate to an integrated chipset, wherein the integrated chipset is within a package enclosing a plurality of vertically stacked packages, wherein the plurality of vertically stacked packages are connected via a common interface.

[0107] In some aspects, the techniques described herein relate to an integrated chipset, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a robot that manages the plurality of robots.

[0108] In some aspects, the techniques described herein relate to an integrated chipset, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a platform that manages the plurality of robots.

[0109] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to recognize objects within the environment.

[0110] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to perform scene understanding.[OHl] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to determine an activity of a human within the environment.

[0112] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to recognize sounds in the environment.

[0113] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to determine a pose of at least one robot of the set of robots.

[0114] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to determine a map of the environment.

[0115] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network control circuit is configured to control navigation within the environment.

[0116] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network control circuit is configured to control interactions with objects in the environment.

[0117] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network control circuit is configured to control interactions with humans in the environment.

[0118] In some aspects, the techniques described herein relate to a method performed by an integrated chipset, the method including: controlling, by a robotic control circuit of the integrated chipset, a set of functions for a set of robots; collecting, by a data collection circuit of the integrated chipset, data from a set of loT devices; processing, by a neural network classifier of the integrated chipset, the data from the loT devices to output a classification of a state of an environment wherein the robots operate; and processing, by a neural network control circuit of the integrated chipset, the output of the neural network classifier to output an operational control parameter to the robotic control circuit, wherein the robotic control circuit, the data collection circuit, the neural network classifier, and the neural network control circuit are configured as layers in a 3D chipset architecture.

[0119] In some aspects, the techniques described herein relate to a method, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a robot that manages the plurality of robots.

[0120] In some aspects, the techniques described herein relate to a method, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a platform that manages the plurality of robots.

[0121] In some aspects, the techniques described herein relate to a method, wherein classifying the state of the environment includes recognizing objects within the environment.

[0122] In some aspects, the techniques described herein relate to a method, wherein classifying the state of the environment includes determining a pose of at least one robot of the set of robots.

[0123] In some aspects, the techniques described herein relate to a method, wherein classifying the state of the environment includes determining a map of the environment.

[0124] In some aspects, the techniques described herein relate to an integrated chipset, including: a robotic control circuit configured to control a set of functions for a set of robots; a data collection circuit configured to handle data from a set of loT devices; a neural network classifier configured to process the data from the loT devices to output a classification of a state of an environment wherein the robots operate; and a neural network control circuit configured to operate on the output of the neural network classifier to output an operational control parameter to the robotic control circuit, wherein the robotic control circuit, the data collection circuit, the neural network classifier and the neural network control circuit share a set of I / O capabilities that are disposed on a perimeter of the chipset.

[0125] In some aspects, the techniques described herein relate to an integrated chipset, wherein the shared set of I / O capabilities include shared I / O ports disposed on a perimeter of the chipset.

[0126] In some aspects, the techniques described herein relate to an integrated chipset, wherein the shared I / O ports are used to send and receive data to and from shared sensors and actuators.

[0127] In some aspects, the techniques described herein relate to an integrated chipset, wherein the shared set of I / O capabilities include the set of functions for the set of robots.

[0128] In some aspects, the techniques described herein relate to an integrated chipset, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a robot that manages the plurality of robots.

[0129] In some aspects, the techniques described herein relate to an integrated chipset, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a platform that manages the plurality of robots.

[0130] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to recognize objects within the environment.

[0131] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to perform scene understanding.

[0132] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to determine an activity of a human within the environment.

[0133] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to recognize sounds in the environment.

[0134] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to determine a pose of at least one robot of the set of robots.

[0135] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to determine a map of the environment.

[0136] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network control circuit is configured to control navigation within the environment.

[0137] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network control circuit is configured to control interactions with objects in the environment.

[0138] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network control circuit is configured to control interactions with humans in the environment.

[0139] In some aspects, the techniques described herein relate to a method performed by an integrated chipset, the method including: controlling, by a robotic control circuit of the integrated chipset, a set of functions for a set of robots; collecting, by a data collection circuit of the integrated chipset, data from a set of loT devices; processing, by a neural network classifier of the integrated chipset, the data from the loT devices to output a classification of a state of an environment wherein the robots operate; and processing, by a neural network control circuit of the integrated chipset, the output of the neural network classifier to output an operational control parameter to the robotic control circuit, wherein the robotic control circuit, the data collection circuit, the neural network classifier and the neural network control circuit share a set of I / O capabilities that are disposed on a perimeter of the chipset.

[0140] In some aspects, the techniques described herein relate to a method, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a robot that manages the plurality of robots.

[0141] In some aspects, the techniques described herein relate to a method, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a platform that manages the plurality of robots.

[0142] In some aspects, the techniques described herein relate to a method, wherein classifying the state of the environment includes recognizing objects within the environment.

[0143] In some aspects, the techniques described herein relate to a method, wherein classifying the state of the environment includes determining a pose of at least one robot of the set of robots.

[0144] In some aspects, the techniques described herein relate to an integrated chipset, including: a robotic control circuit configured to control a set of functions for a set of robots; a data collection circuit configured to handle data from a set of loT devices; a neural network classifier configured to process the data from the loT devices to output a classification of a state of an environment wherein the robots operate; and a neural network control circuit configured to operate on the output of the neural network classifier to output an operational control parameter to the robotic control circuit, wherein the robotic control circuit, the data collection circuit, the neural network classifier and the neural network control circuit share are integrated on the chipset having a set of processing cores concentrated in a middle portion of the chipset served by a set of I / O capabilities located on a perimeter of the chipset with an off-chip interconnection capability substantially at the center of the chipset.

[0145] In some aspects, the techniques described herein relate to an integrated chipset, wherein the shared set of I / O capabilities include shared I / O ports disposed on the perimeter of the chipset.

[0146] In some aspects, the techniques described herein relate to an integrated chipset, wherein the shared I / O ports are used to send and receive data to and from shared sensors and actuators.

[0147] In some aspects, the techniques described herein relate to an integrated chipset, wherein the shared set of I / O capabilities include the set of functions for the set of robots.

[0148] In some aspects, the techniques described herein relate to an integrated chipset, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a robot that manages the plurality of robots.

[0149] In some aspects, the techniques described herein relate to an integrated chipset, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a platform that manages the plurality of robots.

[0150] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to recognize objects within the environment.

[0151] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to perform scene understanding.

[0152] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to determine an activity of a human within the environment.

[0153] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to recognize sounds in the environment.

[0154] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to determine a pose of at least one robot of the set of robots.

[0155] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to determine a map of the environment.

[0156] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network control circuit is configured to control navigation within the environment.

[0157] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network control circuit is configured to control interactions with objects in the environment.

[0158] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network control circuit is configured to control interactions with humans in the environment.

[0159] In some aspects, the techniques described herein relate to a method performed by an integrated chipset, the method including: controlling, by a robotic control circuit of the integrated chipset, a set of functions for a set of robots; collecting, by a data collection circuit of the integrated chipset, data from a set of loT devices; processing, by a neural network classifier of the integrated chipset, the data from the loT devices to output a classification of a state of an environment wherein the robots operate; and processing, by a neural network control circuit of the integrated chipset, the output of the neural network classifier circuit to output an operational control parameter to the robotic control circuit, wherein the robotic control circuit, the data collection circuit, the neural network classifier and the neural network control circuit share are integrated on the chipset having a set of processing cores concentrated in a middle portion of the chipset served by a set of I / O capabilities located on a perimeter of the chipset with an off-chip interconnection capability substantially at the center of the chipset.

[0160] In some aspects, the techniques described herein relate to a method, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a robot that manages the plurality of robots.

[0161] In some aspects, the techniques described herein relate to a method, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a platform that manages the plurality of robots.

[0162] In some aspects, the techniques described herein relate to a method, wherein classifying the state of the environment includes recognizing objects within the environment.

[0163] In some aspects, the techniques described herein relate to a method, wherein classifying the state of the environment includes determining a pose of at least one robot of the set of robots.

[0164] In some aspects, the techniques described herein relate to an integrated chipset, including: a robotic control circuit configured to control a set of functions for a set of robots; a data collection circuit configured to handle data from a set of loT devices; a neural network classifier configured to process the data from the loT devices to output a classification of a state of an environment wherein the robots operate; and a neural network control circuit configured to operate on the output of the neural network classifier to output an operational control parameter to the robotic control circuit, wherein the robotic control circuit, the data collection circuit, the neural network classifier, and the neural network control circuit are integrated on the chipset having a set of modular processing clusters connected by a set of embedded multi -chip interconnect bridges to a set of high bandwidth memory modules.

[0165] In some aspects, the techniques described herein relate to an integrated chipset, wherein a first module processing cluster includes the neural network classifier and the neural network control circuit.

[0166] In some aspects, the techniques described herein relate to an integrated chipset, wherein a second module processing cluster includes the robotic control circuit and the data collection circuit.

[0167] In some aspects, the techniques described herein relate to an integrated chipset, wherein each of the high bandwidth memory modules is a HBM module, a HBM2 module, a HBM2E module, or a HBM3 module.

[0168] In some aspects, the techniques described herein relate to an integrated chipset, wherein the embedded multi-chip interconnect bridge is one of a network on chip (NoC) bridge, an advanced extensible interface (AXI) bridge, a PCI express (PCIe) bridge, a high-speed inter-chip (HSIC) bridge, or a hypertransport bridge.

[0169] In some aspects, the techniques described herein relate to an integrated chipset, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a robot that manages the plurality of robots.

[0170] In some aspects, the techniques described herein relate to an integrated chipset, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a platform that manages the plurality of robots.

[0171] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to recognize objects within the environment.

[0172] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to perform scene understanding.

[0173] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to determine an activity of a human within the environment.

[0174] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to recognize sounds in the environment.

[0175] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to determine a pose of at least one robot of the set of robots.

[0176] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to determine a map of the environment.

[0177] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network control circuit is configured to control navigation within the environment.

[0178] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network control circuit is configured to control interactions with objects in the environment.

[0179] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network control circuit is configured to control interactions with humans in the environment.

[0180] In some aspects, the techniques described herein relate to a method performed by an integrated chipset, the method including: controlling, by a robotic control circuit of the integrated chipset, a set of functions for a set of robots; collecting, by a data collection circuit of the integrated chipset, data from a set of loT devices; processing, by a neural network classifier of the integrated chipset, the data from the loT devices to output a classification of a state of an environment wherein the robots operate; and processing, by a neural network control circuit of the integrated chipset, the output of the neural network classifier to output an operational control parameter to the robotic control circuit, wherein the robotic control circuit, the data collection circuit, the neural network classifier, and the neural network control circuit are integrated on the chipset having a set of modular processing clusters connected by a set of embedded multi -chip interconnect bridges to a set of high bandwidth memory modules.

[0181] In some aspects, the techniques described herein relate to a method, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a robot that manages the plurality of robots.

[0182] In some aspects, the techniques described herein relate to a method, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a platform that manages the plurality of robots.

[0183] In some aspects, the techniques described herein relate to a method, wherein classifying the state of the environment includes recognizing objects within the environment.

[0184] In some aspects, the techniques described herein relate to an integrated chipset, including: a robotic control circuit configured to control a set of functions for a set of robots; a data collection circuit configured to handle data from a set of loT devices; a neural network classifier configured to process the data from the loT devices to output a classification of a state of an environment wherein the robots operate; and a neural network control circuit configured to operate on the output of the neural network classifier to output an operational control parameter to the robotic control circuit, wherein the robotic control circuit, the data collection circuit, theneural network classifier and the neural network control circuit share are integrated on the chipset having a bi-directional torus network on chip architecture.

[0185] In some aspects, the techniques described herein relate to an integrated chipset, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a robot that manages the plurality of robots.

[0186] In some aspects, the techniques described herein relate to an integrated chipset, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a platform that manages the plurality of robots.

[0187] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to recognize objects within the environment.

[0188] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to perform scene understanding.

[0189] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to determine an activity of a human within the environment.

[0190] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to recognize sounds in the environment.

[0191] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to determine a pose of at least one robot of the set of robots.

[0192] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to determine a map of the environment.

[0193] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network control circuit is configured to control navigation within the environment.

[0194] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network control circuit is configured to control interactions with objects in the environment.

[0195] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network control circuit is configured to control interactions with humans in the environment.

[0196] In some aspects, the techniques described herein relate to a method performed by an integrated chipset, the method including: controlling, by a robotic control circuit of the integrated chipset, a set of functions for a set of robots; collecting, by a data collection circuit of the integrated chipset, data from a set of loT devices; processing, by a neural network classifier of the integrated chipset, the data from the loT devices to output a classification of a state of an environment wherein the robots operate; and processing, by a neural network control circuit of the integrated chipset, the output of the neural network classifier to output an operational control parameter to the robotic control circuit, wherein the robotic control circuit, the data collection circuit, the neural network classifier and the neural network control circuit share are integrated on the chipset having a bi-directional torus network on chip architecture.

[0197] In some aspects, the techniques described herein relate to a method, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a robot that manages the plurality of robots.

[0198] In some aspects, the techniques described herein relate to a method, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a platform that manages the plurality of robots.

[0199] In some aspects, the techniques described herein relate to a method, wherein classifying the state of the environment includes recognizing objects within the environment.

[0200] In some aspects, the techniques described herein relate to a method, wherein classifying the state of the environment includes determining a pose of at least one robot of the set of robots.

[0201] In some aspects, the techniques described herein relate to a method, wherein classifying the state of the environment includes determining a map of the environment.

[0202] In some aspects, the techniques described herein relate to a method, wherein the operational control parameter is configured to control navigation within the environment.

[0203] In some aspects, the techniques described herein relate to a method, wherein the operational control parameter is configured to control interactions with humans or objects in the environment.

[0204] In some aspects, the techniques described herein relate to an integrated chipset, including: a robotic control circuit configured to control a set of functions for a set of robots; a data collection circuit configured to handle data from a set of loT devices; a neural network classifier configured to process the data from the loT devices to output a classification of a state of an environment wherein the robots operate; and a neural network control circuit configured to operate on the output of the neural network classifier to output an operational control parameter to the robotic control circuit, wherein the robotic control circuit, the data collection circuit, the neural network classifier, and the neural network control circuit share are integrated on the chipset as a set of modular elements using die-to-die connectivity.

[0205] In some aspects, the techniques described herein relate to an integrated chipset, wherein at least a subset of the modular elements are arranged in a 2.5D or 3D stacked configuration.

[0206] In some aspects, the techniques described herein relate to an integrated chipset, wherein the die-to-die connectivity uses silicon interposers.

[0207] In some aspects, the techniques described herein relate to an integrated chipset, wherein the die-to-die connectivity is one or more of Embedded Multi -Die Interconnect Bridge (EMIB), Advanced Interconnect Bus (AIB), Chip-to-Chip Direct Connect (C2C).

[0208] In some aspects, the techniques described herein relate to an integrated chipset, wherein the chipset is integrated within a package using Wafer-Level Fan-Out (WLFO) and / or Fan-Out Wafer-Level Packaging (FOWLP).

[0209] In some aspects, the techniques described herein relate to an integrated chipset, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a robot that manages the plurality of robots.

[0210] In some aspects, the techniques described herein relate to an integrated chipset, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a platform that manages the plurality of robots.

[0211] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to recognize objects within the environment.

[0212] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to perform scene understanding.

[0213] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to determine an activity of a human within the environment.

[0214] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to recognize sounds in the environment.

[0215] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to determine a pose of at least one robot of the set of robots.

[0216] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to determine a map of the environment.

[0217] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network control circuit is configured to control navigation within the environment.

[0218] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network control circuit is configured to control interactions with objects in the environment.

[0219] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network control circuit is configured to control interactions with humans in the environment.

[0220] In some aspects, the techniques described herein relate to a method performed by an integrated chipset, the method including: controlling, by a robotic control circuit of the integrated chipset, a set of functions for a set of robots; collecting, by a data collection circuit of the integrated chipset, data from a set of loT devices; processing, by a neural network classifier of the integrated chipset, the data from the loT devices to output a classification of a state of an environment wherein the robots operate; and processing, by a neural network control circuit of the integrated chipset, the output of the neural network classifier to output an operational control parameter to the robotic control circuit, wherein the robotic control circuit, the data collection circuit, the neural network classifier, and the neural network control circuit share are integrated on the chipset as a set of modular elements using die-to-die connectivity.

[0221] In some aspects, the techniques described herein relate to a method, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a robot that manages the plurality of robots.

[0222] In some aspects, the techniques described herein relate to a method, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a platform that manages the plurality of robots.

[0223] In some aspects, the techniques described herein relate to a method, wherein classifying the state of the environment includes recognizing objects within the environment.

[0224] In some aspects, the techniques described herein relate to an integrated chipset, including: a robotic control circuit configured to control a set of functions for a set of robots; a data collection circuit configured to handle data from a set of loT devices; a neural network classifier configured to process the data from the loT devices to output a classification of a state of an environment wherein the robots operate; and a neural network control circuit configured to operate on the output of the neural network classifier to output an operational control parameter to the robotic control circuit, wherein the robotic control circuit, the data collection circuit, the neural network classifier and the neural network control circuit share are integrated on the chipset using dynamic voltage and frequency scaling.

[0225] In some aspects, the techniques described herein relate to an integrated chipset, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a robot that manages the plurality of robots.

[0226] In some aspects, the techniques described herein relate to an integrated chipset, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a platform that manages the plurality of robots.

[0227] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to recognize objects within the environment.

[0228] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to perform scene understanding.

[0229] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to determine an activity of a human within the environment.

[0230] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to recognize sounds in the environment.

[0231] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to determine a pose of at least one robot of the set of robots.

[0232] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to determine a map of the environment.

[0233] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network control circuit is configured to control navigation within the environment.

[0234] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network control circuit is configured to control interactions with objects in the environment.

[0235] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network control circuit is configured to control interactions with humans in the environment.

[0236] In some aspects, the techniques described herein relate to a method performed by an integrated chipset, the method including: controlling, by a robotic control circuit of the integrated chipset, a set of functions for a set of robots; collecting, by a data collection circuit of the integrated chipset, data from a set of loT devices; processing, by a neural network classifier of the integrated chipset, the data from the loT devices to output a classification of a state of an environment wherein the robots operate; and processing, by a neural network control circuit of the integrated chipset, the output of the neural network classifier to output an operational control parameter to the robotic control circuit, wherein the robotic control circuit, the data collection circuit, the neural network classifier and the neural network control circuit share are integrated on the chipset using dynamic voltage and frequency scaling.

[0237] In some aspects, the techniques described herein relate to a method, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a robot that manages the plurality of robots.

[0238] In some aspects, the techniques described herein relate to a method, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a platform that manages the plurality of robots.

[0239] In some aspects, the techniques described herein relate to a method, wherein classifying the state of the environment includes recognizing objects within the environment.

[0240] In some aspects, the techniques described herein relate to a method, wherein classifying the state of the environment includes determining a pose of at least one robot of the set of robots.

[0241] In some aspects, the techniques described herein relate to a method, wherein classifying the state of the environment includes determining a map of the environment.

[0242] In some aspects, the techniques described herein relate to a method, wherein the operational control parameter is configured to control navigation within the environment.

[0243] In some aspects, the techniques described herein relate to a method, wherein the operational control parameter is configured to control interactions with humans or objects in the environment.

[0244] In some aspects, the techniques described herein relate to an integrated chipset, including: a robotic control circuit configured to control a set of functions for a set of robots; a data collection circuit configured to handle data from a set of loT devices; a neural network classifier configured to process the data from the loT devices to output a classification of a state of an environment wherein the robots operate; and a neural network control circuit configured to operate on the output of the neural network classifier to output an operational control parameter to the robotic control circuit, wherein the robotic control circuit, the data collection circuit, the neural network classifier and the neural network control circuit share are integrated on an optical chipset where optical communication is partitioned by wavelength to allow selective prioritization by wavelength.

[0245] In some aspects, the techniques described herein relate to an integrated chipset, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a robot that manages the plurality of robots.

[0246] In some aspects, the techniques described herein relate to an integrated chipset, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a platform that manages the plurality of robots.

[0247] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to recognize objects within the environment.

[0248] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to perform scene understanding.

[0249] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to determine an activity of a human within the environment.

[0250] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to recognize sounds in the environment.

[0251] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to determine a pose of at least one robot of the set of robots.

[0252] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to determine a map of the environment.

[0253] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network control circuit is configured to control navigation within the environment.

[0254] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network control circuit is configured to control interactions with objects in the environment.

[0255] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network control circuit is configured to control interactions with humans in the environment.

[0256] In some aspects, the techniques described herein relate to a method performed by an integrated chipset, the method including: controlling, by a robotic control circuit of the integrated chipset, a set of functions for a set of robots; collecting, by a data collection circuit of the integrated chipset, data from a set of loT devices; processing, by a neural network classifier of the integrated chipset, the data from the loT devices to output a classification of a state of an environment wherein the robots operate; and processing, by a neural network control circuit of the integrated chipset, the output of the neural network classifier to output an operational control parameter to the robotic control circuit, wherein the robotic control circuit, the data collection circuit, the neural network classifier and the neural network control circuit share are integrated on an optical chipset where optical communication is partitioned by wavelength to allow selective prioritization by wavelength.

[0257] In some aspects, the techniques described herein relate to a method, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a robot that manages the plurality of robots.

[0258] In some aspects, the techniques described herein relate to a method, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a platform that manages the plurality of robots.

[0259] In some aspects, the techniques described herein relate to a method, wherein classifying the state of the environment includes recognizing objects within the environment.

[0260] In some aspects, the techniques described herein relate to a method, wherein classifying the state of the environment includes determining a pose of at least one robot of the set of robots.

[0261] In some aspects, the techniques described herein relate to a method, wherein classifying the state of the environment includes determining a map of the environment.

[0262] In some aspects, the techniques described herein relate to a method, wherein the operational control parameter is configured to control navigation within the environment.

[0263] In some aspects, the techniques described herein relate to a method, wherein the operational control parameter is configured to control interactions with humans or objects in the environment.

[0264] In some aspects, the techniques described herein relate to an integrated chipset, including: a robotic control circuit configured to control a set of functions for a set of robots; a data collection circuit configured to handle data from a set of loT devices; a neural network classifier configured to process the data from the loT devices to output a classification of a state of an environment wherein the robots operate; and a neural network control circuit configured to operate on the output of the neural network classifier to output an operational control parameter to the robotic control circuit, wherein the robotic control circuit, the data collection circuit, the neural network classifier and the neural network control circuit share are integrated on the chipset using integrated fan-out packaging.

[0265] In some aspects, the techniques described herein relate to an integrated chipset, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a robot that manages the plurality of robots.

[0266] In some aspects, the techniques described herein relate to an integrated chipset, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a platform that manages the plurality of robots.

[0267] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to recognize objects within the environment.

[0268] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to perform scene understanding.

[0269] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to determine an activity of a human within the environment.

[0270] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to recognize sounds in the environment.

[0271] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to determine a pose of at least one robot of the set of robots.

[0272] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to determine a map of the environment.

[0273] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network control circuit is configured to control navigation within the environment.

[0274] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network control circuit is configured to control interactions with objects in the environment.

[0275] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network control circuit is configured to control interactions with humans in the environment.

[0276] In some aspects, the techniques described herein relate to a method performed by an integrated chipset, the method including: controlling, by a robotic control circuit of the integrated chipset, a set of functions for a set of robots; collecting, by a data collection circuit of the integrated chipset, data from a set of loT devices; processing, by a neural network classifier of the integrated chipset, the data from the loT devices to output a classification of a state of an environment wherein the robots operate; and processing, by a neural network control circuit of the integrated chipset, the output of the neural network classifier circuit to output an operational control parameter to the robotic control circuit, wherein the robotic control circuit, the data collection circuit, the neural network classifier and the neural network control circuit share are integrated on the chipset using integrated fan-out packaging.

[0277] In some aspects, the techniques described herein relate to a method, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a robot that manages the plurality of robots.

[0278] In some aspects, the techniques described herein relate to a method, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a platform that manages the plurality of robots.

[0279] In some aspects, the techniques described herein relate to a method, wherein classifying the state of the environment includes recognizing objects within the environment.

[0280] In some aspects, the techniques described herein relate to a method, wherein classifying the state of the environment includes determining a pose of at least one robot of the set of robots.

[0281] In some aspects, the techniques described herein relate to a method, wherein classifying the state of the environment includes determining a map of the environment.

[0282] In some aspects, the techniques described herein relate to a method, wherein the operational control parameter is configured to control navigation within the environment.

[0283] In some aspects, the techniques described herein relate to a method, wherein the operational control parameter is configured to control interactions with humans or objects in the environment.

[0284] In some aspects, the techniques described herein relate to an integrated chipset, including: a robotic control circuit configured to control a set of functions for a set of robots; a data collection circuit configured to handle data from a set of loT devices; a neural network classifier configured to process the data from the loT devices to output a classification of a state of an environment wherein the robots operate; and a neural network control circuit configured to operate on the output of the neural network classifier to output an operational control parameter to the robotic control circuit, wherein the robotic control circuit, the data collection circuit, the neural network classifier and the neural network control circuit are integrated on a high numerical aperture, extreme ultraviolet optical chipset.

[0285] In some aspects, the techniques described herein relate to an integrated chipset, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a robot that manages the plurality of robots.

[0286] In some aspects, the techniques described herein relate to an integrated chipset, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a platform that manages the plurality of robots.

[0287] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to recognize objects within the environment.

[0288] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to perform scene understanding.

[0289] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to determine an activity of a human within the environment.

[0290] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to recognize sounds in the environment.

[0291] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to determine a pose of at least one robot of the set of robots.

[0292] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to determine a map of the environment.

[0293] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network control circuit is configured to control navigation within the environment.

[0294] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network control circuit is configured to control interactions with objects in the environment.

[0295] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network control circuit is configured to control interactions with humans in the environment.

[0296] In some aspects, the techniques described herein relate to a method performed by an integrated chipset, the method including: controlling, by a robotic control circuit of the integrated chipset, a set of functions for a set of robots; collecting, by a data collection circuit of the integrated chipset, data from a set of loT devices; processing, by a neural network classifier of the integrated chipset, the data from the loT devices to output a classification of a state of an environment wherein the robots operate; and processing, by a neural network control circuit of the integrated chipset, the output of the neural network classifier to output an operational control parameter to the robotic control circuit, wherein the robotic control circuit, the data collection circuit, the neural network classifier and the neural network control circuit are integrated on a high numerical aperture, extreme ultraviolet optical chipset.

[0297] In some aspects, the techniques described herein relate to a method, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a robot that manages the plurality of robots.

[0298] In some aspects, the techniques described herein relate to a method, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a platform that manages the plurality of robots.

[0299] In some aspects, the techniques described herein relate to a method, wherein classifying the state of the environment includes recognizing objects within the environment.

[0300] In some aspects, the techniques described herein relate to a method, wherein classifying the state of the environment includes determining a pose of at least one robot of the set of robots.

[0301] In some aspects, the techniques described herein relate to a method, wherein classifying the state of the environment includes determining a map of the environment.

[0302] In some aspects, the techniques described herein relate to a method, wherein the operational control parameter is configured to control navigation within the environment.

[0303] In some aspects, the techniques described herein relate to a method, wherein the operational control parameter is configured to control interactions with humans or objects in the environment.

[0304] In some aspects, the techniques described herein relate to an integrated chipset, including: a robotic control circuit configured to control a set of functions for a set of robots; a data collection circuit configured to handle data from a set of loT devices; a neural network classifier configured to process the data from the loT devices to output a classification of a state of an environment wherein the robots operate; and a neural network control circuit configured to operate on the output of the neural network classifier to output an operational control parameter to the robotic control circuit, wherein the robotic control circuit, the data collection circuit, the neural network classifier and the neural network control circuit are integrated on a chipset using a set of gate-all-around field effect transistors.

[0305] In some aspects, the techniques described herein relate to an integrated chipset, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a robot that manages the plurality of robots.

[0306] In some aspects, the techniques described herein relate to an integrated chipset, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a platform that manages the plurality of robots.

[0307] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to recognize objects within the environment.

[0308] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to perform scene understanding.

[0309] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to determine an activity of a human within the environment.

[0310] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to recognize sounds in the environment.

[0311] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to determine a pose of at least one robot of the set of robots.

[0312] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to determine a map of the environment.

[0313] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network control circuit is configured to control navigation within the environment.

[0314] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network control circuit is configured to control interactions with objects in the environment.

[0315] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network control circuit is configured to control interactions with humans in the environment.

[0316] In some aspects, the techniques described herein relate to a method performed by an integrated chipset, the method including: controlling, by a robotic control circuit of the integrated chipset, a set of functions for a set of robots; collecting, by a data collection circuit of the integrated chipset, data from a set of loT devices; processing, by a neural network classifier of the integrated chipset, the data from the loT devices to output a classification of a state of an environment wherein the robots operate; and processing, by a neural network control circuit of the integrated chipset, the output of the neural network classifier to output an operational control parameter to the robotic control circuit, wherein the robotic control circuit, the data collection circuit, the neural network classifier and the neural network control circuit are integrated on a chipset using a set of gate-all-around field effect transistors.

[0317] In some aspects, the techniques described herein relate to a method, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a robot that manages the plurality of robots.

[0318] In some aspects, the techniques described herein relate to a method, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a platform that manages the plurality of robots.

[0319] In some aspects, the techniques described herein relate to a method, wherein classifying the state of the environment includes recognizing objects within the environment.

[0320] In some aspects, the techniques described herein relate to a method, wherein classifying the state of the environment includes determining a pose of at least one robot of the set of robots.

[0321] In some aspects, the techniques described herein relate to a method, wherein classifying the state of the environment includes determining a map of the environment.

[0322] In some aspects, the techniques described herein relate to a method, wherein the operational control parameter is configured to control navigation within the environment.

[0323] In some aspects, the techniques described herein relate to a method, wherein the operational control parameter is configured to control interactions with humans or objects in the environment.

[0324] In some aspects, the techniques described herein relate to an integrated chipset, including: a robotic control circuit configured to control a set of functions for a set of robots; a data collection circuit configured to handle data from a set of loT devices; a neural network classifier configured to process the data from the loT devices to output a classification of a state of an environment wherein the robots operate; and a neural network control circuit configured to operate on the output of the neural network classifier to output an operational control parameter to the robotic control circuit, wherein the robotic control circuit, the data collection circuit, the neural network classifier and the neural network control circuit are integrated on a chipset using a set of gate-all-around nanowire field effect transistors.

[0325] In some aspects, the techniques described herein relate to an integrated chipset, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a robot that manages the plurality of robots.

[0326] In some aspects, the techniques described herein relate to an integrated chipset, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a platform that manages the plurality of robots.

[0327] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to recognize objects within the environment.

[0328] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to perform scene understanding.

[0329] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to determine an activity of a human within the environment.

[0330] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to recognize sounds in the environment.

[0331] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to determine a pose of at least one robot of the set of robots.

[0332] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to determine a map of the environment.

[0333] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network control circuit is configured to control navigation within the environment.

[0334] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network control circuit is configured to control interactions with objects in the environment.

[0335] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network control circuit is configured to control interactions with humans in the environment.

[0336] In some aspects, the techniques described herein relate to a method performed by an integrated chipset, the method including: controlling, by a robotic control circuit of the integrated chipset, a set of functions for a set of robots; collecting, by a data collection circuit of the integrated chipset, data from a set of loT devices; processing, by a neural network classifier of the integrated chipset, the data from the loT devices to output a classification of a state of an environment wherein the robots operate; and processing, by a neural network control circuit of the integrated chipset, the output of the neural network classifier to output an operational control parameter to the robotic control circuit, wherein the robotic control circuit, the data collection circuit, the neural network classifier and the neural network control circuit are integrated on a chipset using a set of gate-all-around nanowire field effect transistors.

[0337] In some aspects, the techniques described herein relate to a method, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a robot that manages the plurality of robots.

[0338] In some aspects, the techniques described herein relate to a method, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a platform that manages the plurality of robots.

[0339] In some aspects, the techniques described herein relate to a method, wherein classifying the state of the environment includes recognizing objects within the environment.

[0340] In some aspects, the techniques described herein relate to a method, wherein classifying the state of the environment includes determining a pose of at least one robot of the set of robots.

[0341] In some aspects, the techniques described herein relate to a method, wherein classifying the state of the environment includes determining a map of the environment.

[0342] In some aspects, the techniques described herein relate to a method, wherein the operational control parameter is configured to control navigation within the environment.

[0343] In some aspects, the techniques described herein relate to a method, wherein the operational control parameter is configured to control interactions with humans or objects in the environment.

[0344] In some aspects, the techniques described herein relate to an integrated chipset, including: a robotic control circuit configured to control a set of functions for a set of robots; a data collection circuit configured to handle data from a set of loT devices; a neural network classifier configured to process the data from the loT devices to output a classification of a state of an environment wherein the robots operate; and a neural network control circuit configured to operate on the output of the neural network classifier to output an operational control parameter to the robotic control circuit, wherein the robotic control circuit, the data collection circuit, the neural network classifier and the neural network control circuit are integrated on a chipset using a set of gate-all-around nanosheet field effect transistors.

[0345] In some aspects, the techniques described herein relate to an integrated chipset, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a robot that manages the plurality of robots.

[0346] In some aspects, the techniques described herein relate to an integrated chipset, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a platform that manages the plurality of robots.

[0347] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to recognize objects within the environment.

[0348] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to perform scene understanding.

[0349] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to determine an activity of a human within the environment.

[0350] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to recognize sounds in the environment.

[0351] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to determine a pose of at least one robot of the set of robots.

[0352] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to determine a map of the environment.

[0353] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network control circuit is configured to control navigation within the environment.

[0354] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network control circuit is configured to control interactions with objects in the environment.

[0355] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network control circuit is configured to control interactions with humans in the environment.

[0356] In some aspects, the techniques described herein relate to a method performed by an integrated chipset, the method including: controlling, by a robotic control circuit of the integrated chipset, a set of functions for a set of robots; collecting, by a data collection circuit ofthe integrated chipset, data from a set of loT devices; processing, by a neural network classifier of the integrated chipset, the data from the loT devices to output a classification of a state of an environment wherein the robots operate; and processing, by a neural network control circuit of the integrated chipset, the output of the neural network classifier to output an operational control parameter to the robotic control circuit, wherein the robotic control circuit, the data collection circuit, the neural network classifier and the neural network control circuit are integrated on a chipset using a set of gate-all-around nanosheet field effect transistors.

[0357] In some aspects, the techniques described herein relate to a method, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a robot that manages the plurality of robots.

[0358] In some aspects, the techniques described herein relate to a method, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a platform that manages the plurality of robots.

[0359] In some aspects, the techniques described herein relate to a method, wherein classifying the state of the environment includes recognizing objects within the environment.

[0360] In some aspects, the techniques described herein relate to a method, wherein classifying the state of the environment includes determining a pose of at least one robot of the set of robots.

[0361] In some aspects, the techniques described herein relate to a method, wherein classifying the state of the environment includes determining a map of the environment.

[0362] In some aspects, the techniques described herein relate to a method, wherein the operational control parameter is configured to control navigation within the environment.

[0363] In some aspects, the techniques described herein relate to a method, wherein the operational control parameter is configured to control interactions with humans or objects in the environment.

[0364] In some aspects, the techniques described herein relate to an integrated chipset, including: a robotic control circuit configured to control a set of functions for a set of robots; a data collection circuit configured to handle data from a set of loT devices; a neural network classifier configured to process the data from the loT devices to output a classification of a state of an environment wherein the robots operate; and a neural network control circuit configured to operate on the output of the neural network classifier to output an operational control parameter to the robotic control circuit, wherein the robotic control circuit, the data collection circuit, the neural network classifier and the neural network control circuit are integrated on a chipset using a set of gate-all-around complementary field effect transistors.

[0365] In some aspects, the techniques described herein relate to an integrated chipset, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a robot that manages the plurality of robots.

[0366] In some aspects, the techniques described herein relate to an integrated chipset, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a platform that manages the plurality of robots.

[0367] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to recognize objects within the environment.

[0368] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to perform scene understanding.

[0369] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to determine an activity of a human within the environment.

[0370] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to recognize sounds in the environment.

[0371] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to determine a pose of at least one robot of the set of robots.

[0372] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to determine a map of the environment.

[0373] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network control circuit is configured to control navigation within the environment.

[0374] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network control circuit is configured to control interactions with objects in the environment.

[0375] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network control circuit is configured to control interactions with humans in the environment.

[0376] In some aspects, the techniques described herein relate to a method performed by an integrated chipset, the method including: controlling, by a robotic control circuit of the integrated chipset, a set of functions for a set of robots; collecting, by a data collection circuit of the integrated chipset, data from a set of loT devices; processing, by a neural network classifier of the integrated chipset, the data from the loT devices to output a classification of a state of an environment wherein the robots operate; and processing, by a neural network control circuit of the integrated chipset, the output of the neural network classifier to output an operational control parameter to the robotic control circuit, wherein the robotic control circuit, the data collection circuit, the neural network classifier and the neural network control circuit are integrated on a chipset using a set of gate-all-around complementary field effect transistors.

[0377] In some aspects, the techniques described herein relate to a method, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a robot that manages the plurality of robots.

[0378] In some aspects, the techniques described herein relate to a method, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a platform that manages the plurality of robots.

[0379] In some aspects, the techniques described herein relate to a method, wherein classifying the state of the environment includes recognizing objects within the environment.

[0380] In some aspects, the techniques described herein relate to a method, wherein classifying the state of the environment includes determining a pose of at least one robot of the set of robots.

[0381] In some aspects, the techniques described herein relate to a method, wherein classifying the state of the environment includes determining a map of the environment.

[0382] In some aspects, the techniques described herein relate to a method, wherein the operational control parameter is configured to control navigation within the environment.

[0383] In some aspects, the techniques described herein relate to a method, wherein the operational control parameter is configured to control interactions with humans or objects in the environment.

[0384] In some aspects, the techniques described herein relate to an integrated chipset, including: a robotic control circuit configured to control a set of functions for a set of robots; a data collection circuit configured to handle data from a set of loT devices; a neural network classifier configured to process the data from the loT devices to output a classification of a state of an environment wherein the robots operate; and a neural network control circuit configured to operate on the output of the neural network classifier to output an operational control parameter to the robotic control circuit, wherein the robotic control circuit, the data collection circuit, the neural network classifier and the neural network control circuit are integrated on a carbon nanotube chipset.

[0385] In some aspects, the techniques described herein relate to an integrated chipset, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a robot that manages the plurality of robots.

[0386] In some aspects, the techniques described herein relate to an integrated chipset, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a platform that manages the plurality of robots.

[0387] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to recognize objects within the environment.

[0388] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to perform scene understanding.

[0389] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to determine an activity of a human within the environment.

[0390] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to recognize sounds in the environment.

[0391] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to determine a pose of at least one robot of the set of robots.

[0392] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to determine a map of the environment.

[0393] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network control circuit is configured to control navigation within the environment.

[0394] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network control circuit is configured to control interactions with objects in the environment.

[0395] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network control circuit is configured to control interactions with humans in the environment.

[0396] In some aspects, the techniques described herein relate to a method performed by an integrated chipset, the method including: controlling, by a robotic control circuit of the integrated chipset, a set of functions for a set of robots; collecting, by a data collection circuit of the integrated chipset, data from a set of loT devices; processing, by a neural network classifier of the integrated chipset, the data from the loT devices to output a classification of a state of an environment wherein the robots operate; and processing, by a neural network control circuit of the integrated chipset, the output of the neural network classifier to output an operational control parameter to the robotic control circuit, wherein the robotic control circuit, the data collection circuit, the neural network classifier and the neural network control circuit are integrated on a carbon nanotube chipset.

[0397] In some aspects, the techniques described herein relate to a method, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a robot that manages the plurality of robots.

[0398] In some aspects, the techniques described herein relate to a method, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a platform that manages the plurality of robots.

[0399] In some aspects, the techniques described herein relate to a method, wherein classifying the state of the environment includes recognizing objects within the environment.

[0400] In some aspects, the techniques described herein relate to a method, wherein classifying the state of the environment includes determining a pose of at least one robot of the set of robots.

[0401] In some aspects, the techniques described herein relate to a method, wherein classifying the state of the environment includes determining a map of the environment.

[0402] In some aspects, the techniques described herein relate to a method, wherein the operational control parameter is configured to control navigation within the environment.

[0403] In some aspects, the techniques described herein relate to a method, wherein the operational control parameter is configured to control interactions with humans or objects in the environment.

[0404] In some aspects, the techniques described herein relate to an integrated chipset, including: a robotic control circuit configured to control a set of functions for a set of robots; a data collection circuit configured to handle data from a set of loT devices; a neural network classifier configured to process the data from the loT devices to output a classification of a state of an environment wherein the robots operate; and a neural network control circuit configured to operate on the output of the neural network classifier to output an operational control parameter to the robotic control circuit, wherein the robotic control circuit, the data collection circuit, theneural network classifier and the neural network control circuit are integrated on a chipset having high-bandwidth SRAM memory.

[0405] In some aspects, the techniques described herein relate to an integrated chipset, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a robot that manages the plurality of robots.

[0406] In some aspects, the techniques described herein relate to an integrated chipset, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a platform that manages the plurality of robots.

[0407] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to recognize objects within the environment.

[0408] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to perform scene understanding.

[0409] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to determine an activity of a human within the environment.

[0410] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to recognize sounds in the environment.

[0411] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to determine a pose of at least one robot of the set of robots.

[0412] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to determine a map of the environment.

[0413] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network control circuit is configured to control navigation within the environment.

[0414] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network control circuit is configured to control interactions with objects in the environment.

[0415] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network control circuit is configured to control interactions with humans in the environment.

[0416] In some aspects, the techniques described herein relate to a method performed by an integrated chipset, the method including: controlling, by a robotic control circuit of the integrated chipset, a set of functions for a set of robots; collecting, by a data collection circuit of the integrated chipset, data from a set of loT devices; processing, by a neural network classifier of the integrated chipset, the data from the loT devices to output a classification of a state of an environment wherein the robots operate; and processing, by a neural network control circuit of the integrated chipset, the output of the neural network classifier circuit to output an operational control parameter to the robotic control circuit, wherein the robotic control circuit, the data collection circuit, the neural network classifier and the neural network control circuit are integrated on a chipset having high-bandwidth SRAM memory.

[0417] In some aspects, the techniques described herein relate to a method, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a robot that manages the plurality of robots.

[0418] In some aspects, the techniques described herein relate to a method, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a platform that manages the plurality of robots.

[0419] In some aspects, the techniques described herein relate to a method, wherein classifying the state of the environment includes recognizing objects within the environment.

[0420] In some aspects, the techniques described herein relate to a method, wherein classifying the state of the environment includes determining a pose of at least one robot of the set of robots.

[0421] In some aspects, the techniques described herein relate to a method, wherein classifying the state of the environment includes determining a map of the environment.

[0422] In some aspects, the techniques described herein relate to a method, wherein the operational control parameter is configured to control navigation within the environment.

[0423] In some aspects, the techniques described herein relate to a method, wherein the operational control parameter is configured to control interactions with humans or objects in the environment.

[0424] In some aspects, the techniques described herein relate to an integrated chipset, including: a robotic control circuit configured to control a set of functions for a set of robots; a data collection circuit configured to handle data from a set of loT devices; a neural network classifier configured to process the data from the loT devices to output a classification of a state of an environment wherein the robots operate; and a neural network control circuit configured to operate on the output of the neural network classifier to output an operational control parameter to the robotic control circuit, wherein the robotic control circuit, the data collection circuit, the neural network classifier and the neural network control circuit are integrated on a chipset having 3D-NAND flash memory.

[0425] In some aspects, the techniques described herein relate to an integrated chipset, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a robot that manages the plurality of robots.

[0426] In some aspects, the techniques described herein relate to an integrated chipset, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a platform that manages the plurality of robots.

[0427] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to recognize objects within the environment.

[0428] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to perform scene understanding.

[0429] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to determine an activity of a human within the environment.

[0430] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to recognize sounds in the environment.

[0431] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to determine a pose of at least one robot of the set of robots.

[0432] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to determine a map of the environment.

[0433] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network control circuit is configured to control navigation within the environment.

[0434] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network control circuit is configured to control interactions with objects in the environment.

[0435] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network control circuit is configured to control interactions with humans in the environment.

[0436] In some aspects, the techniques described herein relate to a method performed by an integrated chipset, the method including: controlling, by a robotic control circuit of the integrated chipset, a set of functions for a set of robots; collecting, by a data collection circuit of the integrated chipset, data from a set of loT devices; processing, by a neural network classifier of the integrated chipset, the data from the loT devices to output a classification of a state of an environment wherein the robots operate; and processing, by a neural network control circuit of the integrated chipset, the output of the neural network classifier to output an operational control parameter to the robotic control circuit, wherein the robotic control circuit, the data collection circuit, the neural network classifier and the neural network control circuit are integrated on a chipset having 3D-NAND flash memory.

[0437] In some aspects, the techniques described herein relate to a method, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a robot that manages the plurality of robots.

[0438] In some aspects, the techniques described herein relate to a method, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a platform that manages the plurality of robots.

[0439] In some aspects, the techniques described herein relate to a method, wherein classifying the state of the environment includes recognizing objects within the environment.

[0440] In some aspects, the techniques described herein relate to a method, wherein classifying the state of the environment includes determining a pose of at least one robot of the set of robots.

[0441] In some aspects, the techniques described herein relate to a method, wherein classifying the state of the environment includes determining a map of the environment.

[0442] In some aspects, the techniques described herein relate to a method, wherein the operational control parameter is configured to control navigation within the environment.

[0443] In some aspects, the techniques described herein relate to a method, wherein the operational control parameter is configured to control interactions with humans or objects in the environment.

[0444] In some aspects, the techniques described herein relate to an integrated chipset, including: a robotic control circuit configured to control a set of functions for a set of robots; a data collection circuit configured to handle data from a set of loT devices; a neural network classifier configured to process the data from the loT devices to output a classification of a state of an environment wherein the robots operate; and a neural network control circuit configured to operate on the output of the neural network classifier to output an operational control parameter to the robotic control circuit, wherein the robotic control circuit, the data collection circuit, the neural network classifier and the neural network control circuit are integrated on a hybrid- bonded chipset.

[0445] In some aspects, the techniques described herein relate to an integrated chipset, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a robot that manages the plurality of robots.

[0446] In some aspects, the techniques described herein relate to an integrated chipset, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a platform that manages the plurality of robots.

[0447] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to recognize objects within the environment.

[0448] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to perform scene understanding.

[0449] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to determine an activity of a human within the environment.

[0450] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to recognize sounds in the environment.

[0451] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to determine a pose of at least one robot of the set of robots.

[0452] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to determine a map of the environment.

[0453] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network control circuit is configured to control navigation within the environment.

[0454] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network control circuit is configured to control interactions with objects in the environment.

[0455] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network control circuit is configured to control interactions with humans in the environment.

[0456] In some aspects, the techniques described herein relate to a method performed by an integrated chipset, the method including: controlling, by a robotic control circuit of the integrated chipset, a set of functions for a set of robots; collecting, by a data collection circuit of the integrated chipset, data from a set of loT devices; processing, by a neural network classifier of the integrated chipset, the data from the loT devices to output a classification of a state of an environment wherein the robots operate; and processing, by a neural network control circuit of the integrated chipset, the output of the neural network classifier to output an operational control parameter to the robotic control circuit, wherein the robotic control circuit, the data collection circuit, the neural network classifier and the neural network control circuit are integrated on a hybrid-bonded chipset.

[0457] In some aspects, the techniques described herein relate to a method, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a robot that manages the plurality of robots.

[0458] In some aspects, the techniques described herein relate to a method, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a platform that manages the plurality of robots.

[0459] In some aspects, the techniques described herein relate to a method, wherein classifying the state of the environment includes recognizing objects within the environment.

[0460] In some aspects, the techniques described herein relate to a method, wherein classifying the state of the environment includes determining a pose of at least one robot of the set of robots.

[0461] In some aspects, the techniques described herein relate to a method, wherein classifying the state of the environment includes determining a map of the environment.

[0462] In some aspects, the techniques described herein relate to a method, wherein the operational control parameter is configured to control navigation within the environment.

[0463] In some aspects, the techniques described herein relate to a method, wherein the operational control parameter is configured to control interactions with humans or objects in the environment.

[0464] In some aspects, the techniques described herein relate to a robotic fleet including: a plurality of robotic systems, wherein each robotic system of the plurality of robotic systems includes an integrated chipset and a set of sensors, wherein each integrated chipset is configured to generate synthetic training data; and a robotic system in communication with at least a subset of the plurality of robotic systems, wherein the robotic system includes an integrated chipset configured to perform steps including: measuring a performance of an Al model being executed by the integrated chipset; determining that additional training data is required to optimize the performance of the Al model; transmitting, to the subset of the plurality of robotic systems, a request for training data, wherein the request specifies one or more context factors for the robotic system; receiving synthetic training data generated by the subset of the plurality of robotic systems; and fine-tuning the Al model using the synthetic training data to optimize the performance of the Al model.

[0465] In some aspects, the techniques described herein relate to a robotic fleet, wherein the subset of the plurality of robotic systems generate the synthetic training data using an environment digital twin and the one or more context factors.

[0466] In some aspects, the techniques described herein relate to a robotic fleet, wherein the subset of the plurality of robotic systems generate the synthetic training data using a plurality of digital twins.

[0467] In some aspects, the techniques described herein relate to a robotic fleet, wherein the subset of the plurality of robotic systems generates the synthetic training data using generative Al models.

[0468] In some aspects, the techniques described herein relate to a robotic fleet, wherein the generative Al models include one or more of generative adversarial networks (GANs) or large language models (LLMs).

[0469] In some aspects, the techniques described herein relate to a robotic fleet, wherein a first robotic system of the plurality of robotic systems generates a first portion of the synthetic training data based on a first set of sensors on board the first robotic system and a second robotic system of the plurality of robotic systems generates a second portion of the synthetic training data based on a second set of sensors on board the second robotic system, wherein the first set of sensors and the second set of sensors include different sensors.

[0470] In some aspects, the techniques described herein relate to a robotic fleet, wherein a first robotic system of the plurality of robotic systems generates a first portion of the synthetic training data using a first Al model and a second robotic system of the plurality of robotic systems generates a second portion of the synthetic training data using a second Al model, wherein the first Al model and second Al model are different types of Al models.

[0471] In some aspects, the techniques described herein relate to a robotic fleet, wherein the synthetic training data includes data captured by the set of sensors.

[0472] In some aspects, the techniques described herein relate to a robotic fleet, wherein the one or more context factors include a task being performed by the robotic system.

[0473] In some aspects, the techniques described herein relate to a robotic fleet, wherein the subset of the plurality of robotic systems generate the synthetic training data by simulating performance of the task.

[0474] In some aspects, the techniques described herein relate to a robotic fleet, wherein measuring the performance of the Al model includes measuring the performance of the Al model for the task being performed by the robotic system.

[0475] In some aspects, the techniques described herein relate to a robotic fleet, wherein the one or more context factors describe a local environment of the robotic system.

[0476] In some aspects, the techniques described herein relate to a robotic fleet, wherein the one or more context factors describe one or more humans nearby the robotic system.

[0477] In some aspects, the techniques described herein relate to a robotic fleet, wherein the one or more context factors describe one or more devices nearby the robotic system.

[0478] In some aspects, the techniques described herein relate to a robotic fleet, wherein the subset of the plurality of robotic systems generate the synthetic training data by simulating the local environment of the robotic system.

[0479] In some aspects, the techniques described herein relate to a robotic fleet, wherein measuring the performance of the Al model includes measuring the performance of the Al model with respect to the local environment of the robotic system.

[0480] In some aspects, the techniques described herein relate to a robotic fleet, wherein the Al model is one of a navigation model, an object manipulation model, a language model, or a network optimization model.

[0481] In some aspects, the techniques described herein relate to a robotic fleet, wherein the Al model is multimodal, wherein the synthetic training data includes one or more of audio data, image data, or video data.

[0482] In some aspects, the techniques described herein relate to a robotic fleet, wherein the robotic system includes a network enhancement chipset that optimizes the communication with the subset of the plurality of robotic systems.

[0483] In some aspects, the techniques described herein relate to a robotic fleet, wherein the integrated chipset includes a plurality of processing units integrated on a single substrate.

[0484] In some aspects, the techniques described herein relate to a robotic system including: an integrated chipset; and an intelligence layer configured to perform steps including: determining a task for performance by the integrated chipset of the robotic system, wherein the integrated chipset includes a plurality of processing units; selecting an Al model for execution by the integrated chipset for performing the task; obtaining training data for optimizing the Al model's performance on the determined task; causing the integrated chipset to retrain the Al model using the training data; and causing the integrated chipset to execute the retrained Al model to perform the task;

[0485] In some aspects, the techniques described herein relate to a robotic system, wherein obtaining the training data includes retrieving data from storage on board the robotic system.

[0486] In some aspects, the techniques described herein relate to a robotic system, wherein obtaining the training data includes causing the integrated chipset to generate synthetic training data.

[0487] In some aspects, the techniques described herein relate to a robotic system, wherein the integrated chipset generates the synthetic training data using an environment digital twin and one or more context factors for the robotic system.

[0488] In some aspects, the techniques described herein relate to a robotic system, wherein the one or more context factors include one or more requirements, objectives, or methods of performing the task.

[0489] In some aspects, the techniques described herein relate to a robotic system, wherein the one or more context factors describe one or more humans nearby the robotic system.

[0490] In some aspects, the techniques described herein relate to a robotic system, wherein the one or more context factors describe one or more devices nearby the robotic system.

[0491] In some aspects, the techniques described herein relate to a robotic system, wherein the integrated chipset generates the synthetic training data by simulating the performance of the task.

[0492] In some aspects, the techniques described herein relate to a robotic system, wherein the integrated chipset generates the synthetic training data by: transmitting a request for training data to a plurality of other robotic systems in communication with the robotic system; and receiving the synthetic training data from the other robotic systems.

[0493] In some aspects, the techniques described herein relate to a robotic system, wherein the synthetic training data includes data captured by sensors of the other robotic systems.

[0494] In some aspects, the techniques described herein relate to a robotic system, wherein the synthetic training data is generated based on simulations performed by the other robotic systems.

[0495] In some aspects, the techniques described herein relate to a robotic system, wherein causing the integrated chipset to retrain the Al model using the training data includes: selecting a most optimal processing unit of the integrated chipset for retraining; and causing the most optimal processing unit to retrain the Al model using the training data.

[0496] In some aspects, the techniques described herein relate to a robotic system, wherein selecting the most optimal processing unit is based on one or more of processing capability or power efficiency of each processing unit.

[0497] In some aspects, the techniques described herein relate to a robotic system, wherein the most optimal processing unit is an FPGA, wherein causing the most optimal processing unit to retrain the Al model using the training data includes reprogramming the FPGA to execute the retraining.

[0498] In some aspects, the techniques described herein relate to a robotic system, wherein the intelligence layer is further configured to perform steps including: determining that a context for performing the task has ended; and deleting the retrained Al model.

[0499] In some aspects, the techniques described herein relate to a robotic system, wherein the intelligence layer is further configured to perform steps including: measuring the performance of the task; updating the training data in real-time based on the measured performance of the task; and causing the integrated chipset to further retrain the Al model using the updated training data.

[0500] In some aspects, the techniques described herein relate to a robotic system, wherein the Al model is one of a navigation model, an object manipulation model, a language model, or a network optimization model.

[0501] In some aspects, the techniques described herein relate to a robotic system, wherein the Al model is multimodal, wherein the training data includes one or more of audio data, image data, or video data.

[0502] In some aspects, the techniques described herein relate to a robotic system, wherein the plurality of processing units is integrated on a single substrate.

[0503] In some aspects, the techniques described herein relate to a robotic system, wherein the plurality of processing units is manufactured on a single silicon wafer.

[0504] In some aspects, the techniques described herein relate to a robotic fleet including: a fleet management platform configured to assign a plurality of roles to a plurality of robotic systems of the robotic fleet; and a robotic system in communication with the fleet management platform, wherein the robotic system includes an integrated chipset including a plurality of processing units, wherein the robotic system is configured to perform steps including: receiving a role assignment from the fleet management platform; configuring the plurality of processing units to perform tasks associated with the role; determining a next task from a task queue for performance by the robotic system; assigning the next task to a configured processing unit based on one or more context factors; and executing the next task using the configured processing unit.

[0505] In some aspects, the techniques described herein relate to a robotic fleet, wherein the fleet management platform is configured to dynamically re-assign the plurality of roles among the plurality of robotic systems of the robotic fleet based on a current state of the robotic fleet.

[0506] In some aspects, the techniques described herein relate to a robotic fleet, wherein the current state of the robotic fleet includes a status of each robotic system of the plurality of robotic systems.

[0507] In some aspects, the techniques described herein relate to a robotic fleet, wherein the status of each robotic system includes one or more of a power level of each robotic system, an availability to perform additional tasks of each robotic system, and a processing capability of each robotic system.

[0508] In some aspects, the techniques described herein relate to a robotic fleet, wherein the current state of the robotic fleet includes an environment of each robotic system.

[0509] In some aspects, the techniques described herein relate to a robotic fleet, wherein the current state of the robotic fleet includes a set of tasks assigned to the robotic fleet.

[0510] In some aspects, the techniques described herein relate to a robotic fleet, wherein the role assignment is one or more of an enhanced vision role, a data analysis role, or a decisionmaking role.

[0511] In some aspects, the techniques described herein relate to a robotic fleet, wherein the fleet management platform is onboard a controller robotic system.

[0512] In some aspects, the techniques described herein relate to a robotic fleet, wherein the controller robotic system is configured to assign a fleet management role to another robotic system of the robotic fleet.

[0513] In some aspects, the techniques described herein relate to a robotic fleet, wherein the context factors include an Al model used to perform the task.

[0514] In some aspects, the techniques described herein relate to a robotic fleet, wherein the context factors include a power efficiency of the configured processing unit.

[0515] In some aspects, the techniques described herein relate to a robotic fleet, wherein the context factors include a parallelizability of the next task.

[0516] In some aspects, the techniques described herein relate to a robotic fleet, wherein the context factors include one or more timing requirements for the next task.

[0517] In some aspects, the techniques described herein relate to a robotic fleet, wherein the context factors include a communication speed of the configured processing unit.

[0518] In some aspects, the techniques described herein relate to a robotic fleet, wherein the robotic system is further configured to perform steps including, responsive to receiving the role assignment, requesting an Al model associated with the role from another device in communication with the robotic system.

[0519] In some aspects, the techniques described herein relate to a robotic fleet, wherein configuring the plurality of processing units to perform tasks associated with the role includes assigning one or more Al models to one or more processing units.

[0520] In some aspects, the techniques described herein relate to a robotic fleet, wherein assigning one or more Al models to one or more processing units includes reprogramming an FPGA to execute an assigned Al model.

[0521] In some aspects, the techniques described herein relate to a robotic system, wherein the one or more Al models include a navigation model, an object manipulation model, a language model, or a network optimization model.

[0522] In some aspects, the techniques described herein relate to a robotic system, wherein the plurality of processing units is integrated on a single substrate.

[0523] In some aspects, the techniques described herein relate to a robotic system, wherein the plurality of processing units is manufactured on a single silicon wafer.

[0524] In some aspects, the techniques described herein relate to a robotic system including: an integrated chipset including a plurality of processing units; and an intelligence layer configured to perform steps including: receiving a role assignment from a fleet management platform; configuring the plurality of processing units to perform tasks associated with the role; determining a next task from a task queue for performance by the robotic system; selecting an algorithm for execution by the integrated chipset for performing sub-task generation; causing the integrated chipset to execute the selected algorithm to generate one or more sub-tasks for performance of the next task; assigning the one or more sub-tasks to the plurality of processing units; and causing the plurality of processing units of the integrated chipset to execute the one or more sub-tasks to perform the next task.

[0525] In some aspects, the techniques described herein relate to a robotic system, wherein the selected algorithm for performing sub-task generation includes reinforcement learning based on simulating actions within a digital twin environment.

[0526] In some aspects, the techniques described herein relate to a robotic system, wherein the selected algorithm for performing sub-task generation includes one or more of a goal decomposition algorithm, a hierarchical task network, or a genetic algorithm.

[0527] In some aspects, the techniques described herein relate to a robotic system, wherein selecting the algorithm includes selecting an algorithm that is most suitable for sub-task generation based on the next task.

[0528] In some aspects, the techniques described herein relate to a robotic system, wherein selecting the algorithm includes: selecting multiple algorithms for parallel generation of sub-tasks based on the next task; and selecting the one or more sub-tasks based on corresponding sub-tasks generated by the multiple algorithms.

[0529] In some aspects, the techniques described herein relate to a robotic system, wherein the role assignment is one or more of an enhanced vision role, a data analysis role, or a decisionmaking role.

[0530] In some aspects, the techniques described herein relate to a robotic system, wherein the robotic system is further configured to perform steps including, responsive to receiving the role assignment, requesting an Al model associated with the role from another device in communication with the robotic system.

[0531] In some aspects, the techniques described herein relate to a robotic system, wherein configuring the plurality of processing units to perform tasks associated with the role includes assigning one or more Al models to one or more of the plurality of processing units.

[0532] In some aspects, the techniques described herein relate to a robotic fleet, wherein assigning one or more Al models to one or more of the plurality of processing units includes reprogramming an FPGA to execute an assigned Al model.

[0533] In some aspects, the techniques described herein relate to a robotic system, wherein the one or more Al models include a navigation model, an object manipulation model, a language model, or a network optimization model.

[0534] In some aspects, the techniques described herein relate to a robotic system, wherein the one or more sub-tasks include optimization of a network.

[0535] In some aspects, the techniques described herein relate to a robotic system, wherein the one or more sub-tasks include applying a plurality of governance frameworks to control the robotic system.

[0536] In some aspects, the techniques described herein relate to a robotic system, wherein the one or more sub-tasks include a navigation task.

[0537] In some aspects, the techniques described herein relate to a robotic system, wherein the next task includes training an Al model, wherein the one or more sub-tasks include generating synthetic data for training the Al model.

[0538] In some aspects, the techniques described herein relate to a robotic system, wherein the one or more sub-tasks further include requesting synthetic data from other robotic systems in communication with the robotic system.

[0539] In some aspects, the techniques described herein relate to a robotic system, wherein the received role assignment is a fleet controller role, wherein the next task includes dynamically assigning roles to other robotic systems in communication with the robotic system.

[0540] In some aspects, the techniques described herein relate to a robotic system, wherein the intelligence layer is further configured to perform steps including assigning at least one of the sub-tasks to another robotic system.

[0541] In some aspects, the techniques described herein relate to a robotic system, wherein the plurality of processing units is integrated on a single substrate.

[0542] In some aspects, the techniques described herein relate to a robotic system, wherein the plurality of processing units is manufactured on a single silicon wafer.

[0543] In some aspects, the techniques described herein relate to a robotic system, wherein the plurality of processing units are separately manufactured and bonded to a single substrate.

[0544] Brief Description of The Drawings

[0545] The accompanying drawings, which are included to provide a better understanding of the disclosure, illustrate embodiments of the disclosure and together with the description serve to explain the many aspects of the disclosure. In the drawings:

[0546] FIG. 1 is a block diagram showing prior art relationships of various entities and facilities in a supply chain.

[0547] FIG. 2 is a block diagram showing components and interrelationships of systems and processes of a value chain network in accordance with the present disclosure.

[0548] FIG. 3 is another block diagram showing components and interrelationships of systems and processes of a value chain network in accordance with the present disclosure.

[0549] FIG. 4 is a block diagram showing components and interrelationships of systems and processes of a digital products network of FIGS. 2 and 3 in accordance with the present disclosure.

[0550] FIG. 5 is a block diagram showing components and interrelationships of systems and processes of a value chain network technology stack in accordance with the present disclosure.

[0551] FIG. 6 is a block diagram showing a platform and relationships for orchestrating controls of various entities in a value chain network in accordance with the present disclosure.

[0552] FIG. 7 is a block diagram showing components and relationships in embodiments of a value chain network management platform in accordance with the present disclosure.

[0553] FIG. 8 is a block diagram showing components and relationships of value chain entities managed by embodiments of a value chain network management platform in accordance with the present disclosure.

[0554] FIG. 9 is a block diagram showing network relationships of entities in a value chain network in accordance with the present disclosure.

[0555] FIG. 10 is a block diagram showing a set of applications supported by unified data handling layers in a value chain network management platform in accordance with the present disclosure.

[0556] FIG. 11 is a block diagram showing components and relationships in embodiments of a value chain network management platform in accordance with the present disclosure.

[0557] FIG. 12 is a block diagram showing components and relationships of a data storage layer in embodiments of a value chain network management platform in accordance with the present disclosure.

[0558] FIG. 13 is a block diagram showing components and relationships of an adaptive intelligent systems layer in embodiments of a value chain network management platform in accordance with the present disclosure.

[0559] FIG. 14 is a block diagram that depicts providing adaptive intelligence systems for coordinated intelligence for sets of demand and supply applications for a category of goods in accordance with the present disclosure.

[0560] FIG. 15 is a block diagram that depicts providing hybrid adaptive intelligence systems for coordinated intelligence for sets of demand and supply applications or a category of goods in accordance with the present disclosure.

[0561] FIG. 16 is a block diagram that depicts providing adaptive intelligence systems for predictive intelligence for sets of demand and supply applications for a category of goods in accordance with the present disclosure.

[0562] FIG. 17 is a block diagram that depicts providing adaptive intelligence systems for classification intelligence for sets of demand and supply applications for a category of goods in accordance with the present disclosure.

[0563] FIG. 18 is a block diagram that depicts providing adaptive intelligence systems to produce automated control signals for sets of demand and supply applications for a category of goods in accordance with the present disclosure.

[0564] FIG. 19 is a block diagram that depicts training artificial intelligence / machine learning systems to produce information routing recommendations for a selected value chain network in accordance with the present disclosure.

[0565] FIG. 20 is a block diagram that depicts a semi-sentient problem recognition system for recognition of pain points / problem states in a value chain network in accordance with the present disclosure.

[0566] FIG. 21 is a block diagram that depicts a set of artificial intelligence systems operating on value chain information to enable automated coordination of value chain activities for an enterprise in accordance with the present disclosure.

[0567] FIG. 22 is a block diagram showing components and relationships involved in integrating a set of digital twins in an embodiment of a value chain network management platform in accordance with the present disclosure.

[0568] FIG. 23 is a block diagram showing a set of digital twins involved in embodiments of a value chain network management platform in accordance with the present disclosure.

[0569] FIG. 24 is a block diagram showing components and relationships of entity discovery and management systems in embodiments of a value chain network management platform in accordance with the present disclosure.

[0570] FIG. 25 is a block diagram showing components and relationships of a robotic process automation system in embodiments of a value chain network management platform in accordance with the present disclosure.

[0571] FIG. 26 is a block diagram showing components and relationships of a set of opportunity miners in an embodiment of a value chain network management platform in accordance with the present disclosure.

[0572] FIG. 27 is a block diagram showing components and relationships of a set of edge intelligence systems in embodiments of a value chain network management platform in accordance with the present disclosure.

[0573] FIG. 28 is a block diagram showing components and relationships in an embodiment of a value chain network management platform in accordance with the present disclosure.

[0574] FIG. 29 is a block diagram showing additional details of components and relationships in embodiments of a value chain network management platform in accordance with the present disclosure.

[0575] FIG. 30 is a block diagram showing components and relationships in an embodiment of a value chain network management platform that enables centralized orchestration of value chain network entities in accordance with the present disclosure.

[0576] FIG. 31 is a block diagram showing components and relationships of a unified database in an embodiment of a value chain network management platform in accordance with the present disclosure.

[0577] FIG. 32 is a block diagram showing components and relationships of a set of unified data collection systems in embodiments of a value chain network management platform in accordance with the present disclosure.

[0578] FIG. 33 is a block diagram showing components and relationships of a set of Internet of Things monitoring systems in embodiments of a value chain network management platform in accordance with the present disclosure.

[0579] FIG. 34 is a block diagram showing components and relationships of a machine vision system and a digital twin in embodiments of a value chain network management platform in accordance with the present disclosure.

[0580] FIG. 35 is a block diagram showing components and relationships of a set of adaptive edge intelligence systems in embodiments of a value chain network management platform in accordance with the present disclosure.

[0581] FIG. 36 is a block diagram showing additional details of components and relationships of a set of adaptive edge intelligence systems in embodiments of a value chain network management platform in accordance with the present disclosure.

[0582] FIG. 37 is a block diagram showing components and relationships of a set of unified adaptive intelligence systems in embodiments of a value chain network management platform in accordance with the present disclosure.

[0583] FIG. 38 is a schematic of a system configured to train an artificial system that is leveraged by a value chain system using real world outcome data and a digital twin system according to some embodiments of the present disclosure.

[0584] FIG. 39 is a schematic of a system configured to train an artificial system that is leveraged by a container fleet management system using real world outcome data and a digital twin system according to some embodiments of the present disclosure.

[0585] FIG. 40 is a schematic of a system configured to train an artificial system that is leveraged by a logistics design system using real world outcome data and a digital twin system according to some embodiments of the present disclosure.

[0586] FIG. 41 is a schematic of a system configured to train an artificial system that is leveraged by a packaging design system using real world outcome data and a digital twin system according to some embodiments of the present disclosure.

[0587] FIG. 42 is a schematic of a system configured to train an artificial system that is leveraged by a waste mitigation system using real world outcome data and a digital twin system according to some embodiments of the present disclosure.

[0588] FIG. 43 is a schematic illustrating an example of a portion of an information technology system for value chain artificial intelligence leveraging digital twins according to some embodiments of the present disclosure.

[0589] FIG. 44 is a block diagram showing components and relationships of a set of intelligent project management facilities in embodiments of a value chain network management platform in accordance with the present disclosure.

[0590] FIG. 45 is a block diagram showing components and relationships of an intelligent task recommendation system in embodiments of a value chain network management platform in accordance with the present disclosure.

[0591] FIG. 46 is a block diagram showing components and relationships of a routing system among nodes of a value chain network in embodiments of a value chain network management platform in accordance with the present disclosure.

[0592] FIG. 47 is a block diagram showing components and relationships of a dashboard for managing a set of digital twins in embodiments of a value chain network management platform.

[0593] FIG. 48 is a block diagram showing components and relationships in embodiments of a value chain network management platform that uses a microservices architecture.

[0594] FIG. 49 is a block diagram showing components and relationships of an Internet of Things data collection architecture and sensor recommendation system in embodiments of a value chain network management platform.

[0595] FIG. 50 is a block diagram showing components and relationships of a social data collection architecture in embodiments of a value chain network management platform.

[0596] FIG. 51 is a block diagram showing components and relationships of a crowdsourcing data collection architecture in embodiments of a value chain network management platform.

[0597] FIG. 52 is a diagrammatic view that depicts embodiments of a set of value chain network digital twins representing virtual models of a set of value chain network entities in accordance with the present disclosure.

[0598] FIG. 53 is a diagrammatic view that depicts embodiments of a warehouse digital twin kit system in accordance with the present disclosure.

[0599] FIG. 54 is a diagrammatic view that depicts embodiments of a stress test performed on a value chain network in accordance with the present disclosure.

[0600] FIG. 55 is a diagrammatic view that depicts embodiments of methods used by a machine for detecting faults and predicting any future failures of the machine in accordance with the present disclosure.

[0601] FIG. 56 is a diagrammatic view that depicts embodiments of deployment of machine twins to perform predictive maintenance on a set of machines in accordance with the present disclosure.

[0602] FIG. 57 is a schematic illustrating an example of a portion of a system for value chain customer digital twins and customer profile digital twins according to some embodiments of the present disclosure.

[0603] FIG. 58 is a schematic illustrating an example of an advertising application that interfaces with the adaptive intelligent systems layer in accordance with the present disclosure.

[0604] FIG. 59 is a schematic illustrating an example of an e-commerce application integrated with the adaptive intelligent systems layer in accordance with the present disclosure.

[0605] FIG. 60 is a schematic illustrating an example of a demand management application integrated with the adaptive intelligent systems layer in accordance with the present disclosure.

[0606] FIG. 61 is a schematic illustrating an example of a portion of a system for value chain smart supply component digital twins according to some embodiments of the present disclosure.

[0607] FIG. 62 is a schematic illustrating an example of a risk management application that interfaces with the adaptive intelligent systems layer in accordance with the present disclosure.

[0608] FIG. 63 is a diagrammatic view of maritime assets associated with a value chain network management platform including components of a port infrastructure in accordance with the present disclosure.

[0609] FIGS. 64 and 65 are diagrammatic views of maritime assets associated with a value chain network management platform including components of a ship in accordance with the present disclosure.

[0610] FIG. 66 is a diagrammatic view of maritime assets associated with a value chain network management platform including components of a barge in accordance with the present disclosure.

[0611] FIG. 67 is a diagrammatic view of maritime assets associated with a value chain network management platform including those involved in maritime events, legal proceedings and making use of geofenced parameters in accordance with the present disclosure.

[0612] FIG. 68 is a schematic illustrating an example environment of the enterprise and executive control tower and management platform, including data sources in communication therewith, according to some embodiments of the present disclosure.

[0613] FIG. 69 is a schematic illustrating an example set of components of the enterprise control tower and management platform according to some embodiments of the present disclosure.

[0614] FIG. 70 is a schematic illustrating and example of an enterprise data model according to some embodiments of the disclosure.

[0615] FIG. 71 is a schematic illustrating examples of different types of enterprise digital twins, including executive digital twins, in relation to the data layer, processing layer, and application layer of the enterprise digital twin framework according to some embodiments of the present disclosure.

[0616] FIG. 72 is a schematic illustrating an example implementation of the enterprise and executive control tower and management platform according to some embodiments of the present disclosure.

[0617] FIG. 73 is a flow chart illustrating an example set of operations for configuring and serving an enterprise digital twin.

[0618] FIG. 74 illustrates an example set of operations of a method for configuring an organizational digital twin.

[0619] FIG. 75 illustrates an example set of operations of a method for generating an executive digital twin.

[0620] FIGS. 76-103 are schematic diagrams of embodiments of neural net systems that may connect to, be integrated in, and be accessible by the platform for enabling intelligent transactions including ones involving expert systems, self-organization, machine learning, artificial intelligence and including neural net systems trained for pattern recognition, for classification of one or more parameters, characteristics, or phenomena, for support of autonomous control, and other purposes in accordance with embodiments of the present disclosure.

[0621] FIG. 104 is a schematic illustrating an example intelligence services system according to some embodiments of the present disclosure.

[0622] FIG. 105 is a schematic illustrating an example neural network with multiple layers according to some embodiments of the present disclosure.

[0623] FIG. 106 is a schematic illustrating an example convolutional neural network (CNN) according to some embodiments of the present disclosure.

[0624] FIG. 107 is a schematic illustrating an example neural network for implementing natural language processing according to some embodiments of the present disclosure.

[0625] FIG. 108 is a schematic illustrating an example reinforcement learning-based approach for executing one or more tasks by a mobile system according to some embodiments of the present disclosure.

[0626] FIG. 109 is a schematic illustrating an example physical orientation determination chip according to some embodiments of the present disclosure.

[0627] FIG. 110 is a schematic illustrating an example network enhancement chip according to some embodiments of the present disclosure.

[0628] FIG. I l l is a schematic illustrating an example diagnostic chip according to some embodiments of the present disclosure.

[0629] FIG. 112 is a schematic illustrating an example governance chip according to some embodiments of the present disclosure.

[0630] FIG. 113 is a schematic illustrating an example prediction, classification, and recommendation chip according to some embodiments of the present disclosure.

[0631] FIG. 114 is a diagrammatic view illustrating an example environment of an autonomous additive manufacturing platform according to some embodiments of the present disclosure.

[0632] FIG. 115 is a schematic illustrating an example implementation of an autonomous additive manufacturing platform for automating and optimizing the digital production workflow for metal additive manufacturing according to some embodiments of the present disclosure.

[0633] FIG. 116 is a flow diagram illustrating the optimization of different parameters of an additive manufacture process according to some embodiments of the present disclosure.

[0634] FIG. 117 is a schematic view illustrating a system for learning on data from an autonomous additive manufacturing platform to train an artificial learning system to use digital twins for classification, predictions and decision making according to some embodiments of the present disclosure.

[0635] FIG. 118 is a schematic illustrating an example implementation of an autonomous additive manufacturing platform including various components along with other entities of a distributed manufacturing network according to some embodiments of the present disclosure.

[0636] FIG. 119 is a schematic illustrating an example implementation of an autonomous additive manufacturing platform for automating and managing manufacturing functions and sub- processes including process and material selection, hybrid part workflows, feedstock formulation, part design optimization, risk prediction and management, marketing and customer service according to some embodiments of the present disclosure.

[0637] FIG. 120 is a diagrammatic view of a distributed manufacturing network enabled by an autonomous additive manufacturing platform and built on a distributed ledger system according to some embodiments of the present disclosure.

[0638] FIG. 121 is a schematic illustrating an example implementation of a distributed manufacturing network where the digital thread data is tokenized and stored in a distributed ledger so as to ensure traceability of parts printed at one or more manufacturing nodes in the distributed manufacturing network according to some embodiments of the present disclosure.

[0639] FIG. 122 is a diagrammatic view illustrating an example implementation of a conventional computer vision system for creating an image of an object of interest.

[0640] FIG. 123 is a schematic illustrating an example implementation of a dynamic vision system for dynamically learning an object concept about an object of interest according to some embodiments of the present disclosure.

[0641] FIG. 124 is a schematic illustrating an example architecture of a dynamic vision system according to some embodiments of the present disclosure.

[0642] FIG. 125 is a flow diagram illustrating a method for object recognition by a dynamic vision system according to some embodiments of the present disclosure.

[0643] FIG. 126 is a schematic illustrating an example implementation of a dynamic vision system for modelling, simulating and optimizing various optical, mechanical, design andlighting parameters of the dynamic vision system according to some embodiments of the present disclosure.

[0644] FIG. 127 is a schematic view illustrating an example implementation of a dynamic vision system depicting detailed view of various components along with integration of the dynamic vision system with one or more third party systems according to some embodiments of the present disclosure.

[0645] FIG. 128 is a schematic illustrating an example environment of a fleet management platform according to some embodiments of the present disclosure.

[0646] FIG. 129 is a schematic illustrating example configurations of a multi-purpose robot and a special purpose robot according to some embodiments of the present disclosure.

[0647] FIG. 130 is a schematic illustrating an example platform -level intelligence layer of a fleet management platform according to some embodiments of the present disclosure.

[0648] FIG. 131 is a schematic illustrating an example configuration of an intelligence layer according to some embodiments of the present disclosure.

[0649] FIG. 132 is a schematic illustrating an example security framework according to some embodiments of the present disclosure.

[0650] FIG. 133 is a schematic illustrating an example environment of a fleet management platform according to some embodiments of the present disclosure.

[0651] FIG. 134 is a schematic illustrating an example data flow of a job configuration system according to some embodiments of the present disclosure.

[0652] FIG. 135 is a schematic illustrating an example data flow of a fleet operations system according to some embodiments of the present disclosure.

[0653] FIG. 136 is a schematic illustrating an example job parsing system and task definition system and an example data flow thereof according to some embodiments of the present disclosure.

[0654] FIG. 137 is a schematic illustrating an example fleet configuration system and an example data flow thereof according to some embodiments of the present disclosure.

[0655] FIG. 138 is a schematic illustrating an example workflow definition system and an example data flow thereof according to some embodiments of the present disclosure.

[0656] FIG. 139 is a schematic illustrating example configurations of a multi-purpose robot and components thereof according to some embodiments of the present disclosure.

[0657] FIG. 140 is a schematic illustrating an example architecture of the robot control system according to some embodiments of the present disclosure.

[0658] FIG. 141 is a schematic illustrating an example architecture of the robot control system 12150 that utilizes data from multiple sensors in the vision and sensing system according to some embodiments of the present disclosure.

[0659] FIG. 142 is a schematic illustrating an example vision and sensing system of a robot according to some embodiments of the present disclosure.

[0660] FIG. 143 is a schematic illustrating an example process that is executed by a multipurpose robot to harvest crops according to some embodiments of the present disclosure.

[0661] FIG. 144 is a schematic illustrating an example environment of the intermodal smart container system according to some embodiments of the present disclosure.

[0662] FIG. 145 is a schematic illustrating example configurations of a smart container according to some embodiments of the present disclosure.

[0663] FIG. 146 is a schematic illustrating an intelligence service adapted to provide intelligence services to the smart intermodal container system according to some embodiments of the present disclosure.

[0664] FIG. 147 is a schematic illustrating a digital twin module according to some embodiments of the present disclosure according to some embodiments of the present disclosure.

[0665] FIG. 148 illustrates an example embodiment of a method of receiving requests to update one or more properties of digital twins of shipping entities and / or environments.

[0666] FIG. 149 illustrates an example embodiment of a method for updating a set of cost of downtime values in the digital twin of a smart container according to some embodiments of the present disclosure.

[0667] FIG. 150 is a schematic illustrating an example environment of a digital product network according to some embodiments of the present disclosure.

[0668] FIG. 151 is a schematic illustrating an example environment of a connected product according to some embodiments of the present disclosure.

[0669] FIG. 152 is a schematic illustrating an example environment of a digital product network according to some embodiments of the present disclosure.

[0670] FIG. 153 is a schematic illustrating an example environment of a digital product network according to some embodiments of the present disclosure.

[0671] FIG. 154 is a flow diagram illustrating a method of using product level data according to some embodiments of the disclosure.

[0672] FIG. 155 is a schematic illustrating an example environment of a digital product network according to some embodiments of the present disclosure.

[0673] FIG. 156 is a schematic illustrating an example of a smart futures contract system according to some embodiments of the present disclosure.

[0674] FIG. 157 is a schematic illustrating an example environment of an edge networking system according to some embodiments of the present disclosure.

[0675] FIG. 158 is a schematic illustrating an example environment of an edge networking system including a VCN bus according to some embodiments of the present disclosure.

[0676] FIG. 159 is a schematic illustrating an example environment of an edge networking system according to some embodiments of the present disclosure including a configured device EDNW system.

[0677] FIG. 160 is a block diagram showing a schematic of a dual-process artificial neural network system according to some embodiments of the present disclosure.

[0678] FIG. 161 is a schematic view of an example control tower dashboard for one or more VCN processes that may be used with one or more example implementations of the disclosure.

[0679] FIG. 162 is an example flowchart of one or more VCN processes that may be used with one or more example implementations of the disclosure.

[0680] FIG. 163 A is a schematic view of an example control architecture for system facilitation and / or management.

[0681] FIG. 163B is a schematic view of another example control architecture for system facilitation and / or management.

[0682] FIG. 163C is a schematic view of an example control architecture for system facilitation and / or management.

[0683] FIG. 163D is a schematic view of another example control architecture for system facilitation and / or management.

[0684] FIG. 164A is a schematic view of an example management stack that includes a control architecture similar to FIGS. 163A and 163B.

[0685] FIG. 164B is a schematic view of an example management stack capable of implementing a control architecture similar to that of FIGS. 163 A and 163B for a value chain network.

[0686] FIG. 164C is a schematic view of an example management stack that includes a control architecture similar to FIGS. 163C and 163D.

[0687] FIG. 164D is a schematic view of an example management stack capable of implementing a control architecture similar to that of FIGS. 163C and 163D for a value chain network.

[0688] FIG. 165 A is a flow diagram of an example arrangement for a control architecture similar to that of FIGS. 163A and 163B.

[0689] FIG. 165B is a flow diagram of an example arrangement for a control architecture similar to that of FIGS. 163C and 163D.

[0690] FIG. 166 is an example flowchart of one or more VCN processes that may be used with one or more example implementations of the disclosure.

[0691] FIGS. 167-173 are example flowcharts of one or more VCN processes that may be used with one or more example implementations of the disclosure.

[0692] FIG. 174 is a schematic view of an example generative Al system.

[0693] FIG. 175 is a schematic view of an example of a determination of attention by a machine learning model.

[0694] FIG. 176 is a schematic view of an example of a transformer model.

[0695] FIG. 177 is a schematic view of a value chain network (VCN) converging technology stack.

[0696] FIG. 178 is a schematic view of an example robotic system.

[0697] FIG. 179 is a schematic view of an example robotic fleet operations platform.

[0698] FIG. 180 is a schematic view of an example specialized integrated chipset.

[0699] FIG. 181 is a schematic view of an example robot and / or robot fleet management platform.

[0700] FIG. 182 is a schematic view of an example environment including a fleet management platform and / or robot.

[0701] FIG. 183 is a schematic view of an example environment including a fleet management platform and / or robot.

[0702] Like reference symbols in the various drawings indicate like elementsDETAILED DESCRIPTION

[0703] In example embodiments, systems and processes of this disclosure may include information technology processes and systems for management of value chain network entities, including supply chain and demand management entities. In example embodiments, enterprise management platforms, more particularly involving an edge-distributed database and query language for storing and retrieving value chain data may also be used.

[0704] Orders for products were fulfilled by manufacturers through a supply chain, such as depicted in FIG. 1, where suppliers 122 in various supply environments 160, operating production facilities 134 or acting as resellers or distributors for others, made a product 130 available at a point of origin 102 in response to an order. The product 130 was passed through the supply chain, being conveyed and stored via various hauling facilities 138 and distribution facilities 134, such as warehouses 132, fulfillment centers 112 and delivery systems 114, such as trucks and other vehicles, trains, and the like. In many cases, maritime facilities and infrastructure, such as ships, barges, docks and ports provided transport over waterways between the points of origin 102 and one or more destinations 104.

[0705] Organizations have access to an almost unlimited amount of data. With the advent of smart connected devices, wearable technologies, the Internet of Things (loT), and the like, the amount of data available to an organization that is planning, overseeing, managing and operating a value chain network has increased dramatically and will likely continue to do so. For example, in a manufacturing facility, warehouse, campus, or other operating environment, there may be hundreds to thousands of loT sensors that provide metrics such as vibration data that measure the vibration signatures of important machinery, temperatures throughout the facility, motion sensors that can track throughput, asset tracking sensors and beacons to locate items, cameras and optical sensors, chemical and biological sensors, and many others. Additionally, as wearable technologies become more prevalent, wearables may provide insight into the movement, health indicators, physiological states, activity states, movements, and other characteristics of workers. Furthermore, as organizations implement CRM systems, ERP systems, operations systems, information technology systems, advanced analytics and other systems that leverage information and information technology, organizations have access to an increasingly wide array of other large data sets, such as marketing data, sales data, operational data, information technology data, performance data, customer data, financial data, market data, pricing data, supply chain data, and the like, including data sets generated by or for the organization and third-party data sets.

[0706] The presence of more data and data of new types offers many opportunities for organizations to achieve competitive advantages; however, it also presents problems, such as ofcomplexity and volume, such that users can be overwhelmed, missing opportunities for insight. A need exists for methods and systems that allow enterprises not only to obtain data, but to convert the data into insights and to translate the insights into well-informed decisions and timely execution of efficient operations.

[0707] Acquiring large data sets from thousands, or potentially millions of devices (containing large numbers of sensors) distributed across multiple organizations in a value chain network has become more typical. For example, there is a proliferation of Radio Frequency Identification (RFID) Tags to individual goods in retail stores. In this situation and other similar situations, a vast number of data streams can overwhelm the ability to transmit the data across networks and / or the ability to create effective automated centralized decisions.

[0708] The proliferation of data generators (e.g., sensors) has created an opportunity to manage networks such as value chain networks with input from massive numbers of distributed points of semi -intelligent control. However, current approaches often rely on limited centralized data collection due to bandwidth, storage, processing, and / or other limitations.

[0709] Over time, companies have increasingly used technology solutions to improve outcomes related to a traditional supply chain like the one depicted in FIG. 1, such as software systems for predicting and managing customer demand, RFID and asset tracking systems for tracking goods as they move through the supply chain, navigation and routing systems to improve the efficiency of route selection, and the like. However, some large trends have placed manufacturers, retailers and other businesses under increasing pressure to improve supply chain performance. First, online and ecommerce operators, in particular Amazon™ have become the largest retail channels for many categories of goods and have introduced distribution and fulfillment centers 112 throughout some geographies like the United States that house hundreds of thousands, and sometimes more, product categories (SKUs), so that customers can receive items the day after they are ordered, and in some cases on the same day (and in some cases delivered to the door by a drone, robot, and / or autonomous vehicle. For retailers that do not have extensive geographic distribution of fulfillment centers or warehouses, customer expectations for speed of delivery place increased pressure on supply chain efficiency and optimization. Accordingly, a need still exists for improved supply chain methods and systems.

[0710] Second, agile manufacturing capabilities (such as using 3D printing and robotic assembly techniques, among others), customer profiling technologies, and online ratings and reviews have led to increased customer expectations for customization and personalization of products. Accordingly, in order to compete, manufacturers and retailers need improved methods and systems for understanding, predicting, and satisfying customer demand.

[0711] Historically, supply chain management and demand planning and management have been largely separate activities, unified primarily when demand is converted to an order, which is passed to the supply side for fulfillment in a supply chain. As expectations for speed and personalization increase, a need exists for methods and systems that can provide unified orchestration of supply and demand.

[0712] In parallel with these other large trends has been the emergence of the Internet of Things, in which some categories of products, particularly smart home products like thermostats, lighting systems, and speakers, are increasingly enabled with onboard network connectivity and processing capability, often including a voice controlled intelligent agent like Alexa™ or Siri™ that allows device control and triggering of certain application features, such as playing music, or even ordering a product. In some cases, smart products 650 even initiate orders, such as printers that order refill cartridges. Intelligent products 650 are in some cases involved in a coordinated system, such as where an Amazon™ Echo™ product controls a television, or where a sensor-enabled thermostat or security camera connects to a mobile device, but most intelligent products are still involved in sets of largely isolated, application-specific interactions. As artificial intelligence capabilities increase, and as more and more computing and networking power is moved to network-enabled edge devices and systems that reside in supply environments 670, in demand environments 672, and in all of the locations, systems, and facilities that populate the path of a product 1510 from the loading dock of a manufacturer to the point of destination 612 of a customer 662 or retailers 664, a need and opportunity exists for dramatically improved intelligence, control, and automation of all of the factors involved in demand and supply.VALUE CHAIN NETWORKS

[0713] Referring to FIG. 2, a block diagram is presented at 200 showing components and interrelationships of systems and processes of a value chain network. In example embodiments, “value chain network,” as used herein, refers to elements and interconnections of historically segregated demand management systems and processes and supply chain management systems and processes, enabled by the development and convergence of numerous diverse technologies. In example embodiments a value chain control tower 260 (e.g., referred to herein in some cases as a “value chain network management platform”, a “VCNP”, or simply as “the system”, or “the platform”) may be connected to, in communication with, or otherwise operatively coupled with data processing facilities including, but not limited to, big data centers (e.g., big data processing 230) and related processing functionalities that receive data flow, data pools, data streams and / or other data configurations and transmission modalities received from, for example, digital product networks 21002, directly from customers (e.g., direct connected customer 250), or some other third party 220. Communications related to market orchestration activities and communications 210, analytics 232, or some other type of input may also be utilized by the value chain control tower for demand enhancement 262, synchronized planning 234, intelligent procurement 238, dynamic fulfillment 240 or some other smart operation informed by coordinated and adaptive intelligence, as described herein.

[0714] Referring to FIG. 3, another block diagram is presented showing components and interrelationships of systems and processes of a value chain network and related uses cases, data handling, and associated entities. In example embodiments, the value chain control tower 360 may coordinate market orchestration activities 310 including, but not limited to, demand curvemanagement 352, synchronization of an ecosystem 348, intelligent procurement 344, dynamic fulfillment 350, value chain analytics 340, and / or smart supply chain operations 342. In example embodiments, the value chain control tower 360 may be connected to, in communication with, or otherwise operatively coupled with adaptive data pipelines 302 and processing facilities that may be further connected to, in communication with, or otherwise operationally coupled with external data sources 320 and a data handling stack 330 (e.g., value chain network technology) that may include intelligent, user-adaptive interfaces, adaptive intelligence and control 332, and / or adaptive data monitoring and storage 334, as described herein. The value chain control tower 302 may also be further connected to, in communication with, or otherwise operatively coupled with additional value chain entities including, but not limited to, digital product networks 21002, customers (e.g., directed connected customers 362), and / or other connected operations 364 and entities of a value chain network.DIGITAL PRODUCT NETWORKS (“DPN”)

[0715] Referring to FIG. 4, a block diagram is presented showing components and interrelationships of systems and processes of the digital products networks at 400. In example embodiments, products (including goods and services) may create and transmit data, such as product level data, to a communication layer within the value chain network technology stack and / or to an edge data processing facility. This data may produce enhanced product level data and may be combined with third party data for further processing, modeling or other adaptive or coordinated intelligence activity, as described herein. This may include, but is not limited to, producing and / or simulating product and value chain use cases, the data for which may be utilized by products, product development processes, product design, and the like.STACK VIEW EXAMPLES

[0716] Referring to FIG. 5, a block diagram is presented at 500 showing components and interrelationships of systems and processes of a value chain network technology stack, which may include, but is not limited to a presentation layer, an intelligence layer, and serverless functionalities such as platforms (e.g., development and hosting platforms), data facilities (e.g., relating to data with loT and Big Data), and data aggregation facilities. In example embodiments, the presentation layer may include, but is not limited to, a user interface, and modules for investigation and discovery and tracking users’ experience and engagements. In example embodiments, the intelligence layer may include, but is not limited to, a statistical and computation methods, semantic models, an analytics library, a development environment for analytics, algorithms, logic and rules, and machine learning. In example embodiments, the platforms or the value chain network technology stack may include a development environment, APIs for connectivity, cloud and / or hosting applications, and device discovery. In example embodiments, the data aggregation facilities or layer may include, but is not limited to, modules for data normalization for common transmission and heterogeneous data collection from disparate devices. In example embodiments, the data facilities or layer may include, but is not limited to, loT and big data access, control, and collection and alternatives. In exampleembodiments, the value chain network technology stack may be further associated with additional data sources and / or technology enablers.VALUE CHAIN ORCHESTRATION FROM A COMMAND PLATFORM

[0717] FIG. 6 illustrates a connected value chain network 668 in which a value chain network management platform 604 (referred to herein in some cases as a “value chain control tower,” the “VCNP,” or simply as “the system,” or “the platform”) orchestrates a variety of factors involved in planning, monitoring, controlling, and optimizing various entities and activities involved in the value chain network 668, such as supply and production factors, demand factors, logistics and distribution factors, and the like. By virtue of a unified platform 604 for monitoring and managing supply factors and demand factors as well as status information (e.g., quality and status, plan, order and confirm, and / or track and trace) can be shared about and between various entities (e.g., including custom ers / consumers, suppliers, distribution such as distributors, suppliers, and production such as producers or production facilities) as demand factors are understood and accounted for, as orders are generated and fulfilled, and as products are created and moved through a supply chain. The value chain network 668 may include not only an intelligent product 1510, but all of the equipment, infrastructure, personnel and other entities involved in planning and satisfying demand for it.VALUE CHAIN NETWORK AND VALUE CHAIN NETWORK MANAGEMENT PLATFORM

[0718] Referring to FIG. 7, the value chain network 668 managed by a value chain management platform 604 may include a set of value chain network entities 652, such as, without limitation: a product 1510, which may be an intelligent product 1510; a set of production facilities 674 involved in producing finished goods, components, systems, subsystems, materials used in goods, or the like; various entities, activities and other supply factors 648 involved in supply environments 670, such as suppliers 642, points of origin 610, and the like; various entities, activities and other demand factors 644 involved in demand environments 672, such as customers 662 (including consumers, businesses, and intermediate customers such as value added resellers and distributors), retailers 664 (including online retailers, mobile retailers, conventional bricks and mortar retailers, pop-up shops and the like) and the like located and / or operating at various destinations 612; various distribution environments 678 and distribution facilities 658, such as warehousing facilities 654, fulfillment facilities 628, and delivery systems 632, and the like, as well as maritime facilities 622, such as port infrastructure facilities 660, floating assets 620, and shipyards 638, among others. In embodiments, the value chain network management platform 604 monitors, controls, and otherwise enables management (and in some cases autonomous or semi -autonomous behavior) of a wide range of value chain network 668 processes, workflows, activities, events and applications 630 (collectively referred to in some cases simply as “applications 630”).

[0719] Referring still to FIG. 7, a high-level schematic of the value chain network management platform 604 is illustrated. The value chain network management platform 604 may include a set of systems, applications, processes, modules, services, layers, devices,components, machines, products, sub-systems, interfaces, connections, and other elements working in coordination to enable intelligent management of a set of value chain entities 652 that may occur, operate, transact or the like within, or own, operate, support or enable, one or more value chain network processes, workflows, activities, events and / or applications 630 or that may otherwise be part of, integrated with, linked to, or operated on by the VCNP 604 in connection with a product 1510 (which may be any category of product, such as a finished good, software product, hardware product, component product, material, item of equipment, item of consumer packaged goods, consumer product, food product, beverage product, home product, business supply product, consumable product, pharmaceutical product, medical device product, technology product, entertainment product, or any other type of product and / or set of related services, and which may, in embodiments, encompass an intelligent product 1510 that is enabled with a set of capabilities such as, without limitation data processing, networking, sensing, autonomous operation, intelligent agent, natural language processing, speech recognition, voice recognition, touch interfaces, remote control, self-organization, self-healing, process automation, computation, artificial intelligence, analog or digital sensors, cameras, sound processing systems, data storage, data integration, and / or various Internet of Things capabilities, among others.

[0720] In embodiments, the management platform 604 may include a set of data handling layers 608 each of which is configured to provide a set of capabilities that facilitate development and deployment of intelligence, such as for facilitating automation, machine learning, applications of artificial intelligence, intelligent transactions, state management, event management, process management, and many others, for a wide variety of value chain network applications and end uses. In embodiments, the data handling layers 608 are configured in a topology that facilitates shared data collection and distribution across multiple applications and uses within the platform 604 by a value chain monitoring systems layer 614. The value chain monitoring systems layer 614 may include, integrate with, and / or cooperate with various data collection and management systems 640, referred to for convenience in some cases as data collection systems 640, for collecting and organizing data collected from or about value chain entities 652, as well as data collected from or about the various data layers 624 or services or components thereof. In embodiments, the data handling layers 608 are configured in a topology that facilitates shared or common data storage across multiple applications and uses of the platform 604 by a value chain network-oriented data storage systems layer 624, referred to herein for convenience in some cases simply as a data storage layer 624 or storage layer 624. As shown in FIG. 7, the data handling layers 608 may also include an adaptive intelligent systems layer 614. The adaptive intelligence systems layer 614 may include a set of data processing, artificial intelligence and computational systems 634 that are described in more detail elsewhere throughout this disclosure. The data processing, artificial intelligence and computational systems 634 may relate to artificial intelligence (e.g., expert systems, artificial intelligence, neural, supervised, machine learning, deep learning, model-based systems, and the like). Specifically, the data processing, artificial intelligence and computational systems 634 may relate to variousexamples, in some embodiments, such as use of a recurrent network as adaptive intelligence system operating on a blockchain of transactions in a supply chain to determine a pattern, use with biological systems, opportunity mining (e.g., where artificial intelligence system may be used to monitor for new data sources as opportunities for automatically deploying intelligence), robotic process automation (e.g., automation of intelligent agents for various workflows), edge and network intelligence (e.g., implicated on monitoring systems such as adaptively using available RF spectrum, adaptively using available fixed network spectrum, adaptively storing data based on available storage conditions, adaptively sensing based on a kind of contextual sensing), and the like.

[0721] In embodiments, the data handling layers 608 may be depicted in vertical stacks or ribbons in the figures and may represent many functionalities available to the platform 604 including storage, monitoring, and processing applications and resources and combinations thereof. In embodiments, the set of capabilities of the data handling layers 608 may include a shared microservices architecture. By way of these examples, the set of capabilities may be deployed to provide multiple distinct services or applications, which can be configured as one or more services, workflows, or combinations thereof. In some examples, the set of capabilities may be deployed within or be resident to certain applications or processes. In some examples, the set of capabilities can include one or more activities marshaled for the benefit of the platform. In some examples, the set of capabilities may include one or more events organized for the benefit of the platform. In embodiments, one of the sets of capabilities of the platform may be deployed within at least a portion of a common architecture such as common architecture that supports a common data schema. In embodiments, one of the sets of capabilities of the platform may be deployed within at least a portion of a common architecture that can support a common storage. In embodiments, one of the sets of capabilities of the platform may be deployed within at least a portion of a common architecture that can support common monitoring systems. In embodiments, one or more sets of capabilities of the platform may be deployed within at least a portion of a common architecture that can support one or more common processing frameworks. In embodiments, the set of capabilities of the data handling layers 608 can include examples where the storage functionality supports scalable processing capabilities, scalable monitoring systems, digital twin systems, payments interface systems, and the like. By way of these examples, one or more software development kits can be provided by the platform along with deployment interfaces to facilitate connections and use of the capabilities of the data handling layers 608. In further examples, adaptive intelligence systems may analyze, learn, configure, and reconfigure one or more of the capabilities of the data handling layers 608. In embodiments, the platform 604 may, for example, include a common data storage schema serving a shipyard entity related service and a warehousing entity service. There are many other applicable examples and combinations applicable to the foregoing example including the many value chain entities disclosed herein. By way of these examples, the platform 604 may be shown to create connectivity (e.g., supply of capabilities and information) across many value chain entities. In many examples, there are pairings (doubles, triples, quadruplets, etc.) of similar kinds of valuechain entities using one or more smaller sets of capabilities of the data handling layers 608 to deploy (interact with, rely on, etc.) a common data schema, a common architecture, a common interface, and the like. While services and capabilities can be provided to single value chain entities, the platform can be shown to provide myriad benefits to value chains and consumers by supporting connectivity across value chain entities and applications used by the entities.VALUE CHAIN NETWORK ENTITIES MANAGED BY THE PLATFORM

[0722] Referring to FIG. 8, the value chain network management platform 604 is illustrated in connection with a set of value chain entities 652 that may be subject to management by the platform 604, may integrate with or into the platform 604, and / or may supply inputs to and / or take outputs from the platform 604, such as ones involved in or for a wide range of value chain activities (such as supply chain activities, logistics activities, demand management and planning activities, delivery activities, shipping activities, warehousing activities, distribution and fulfillment activities, inventory aggregation, storage and management activities, marketing activities, and many others, as involved in various value chain network processes, workflows, activities, events and applications 630 (collectively “applications 630” or simply “activities”)). Connections with the value chain entities 652 may be facilitated by a set of connectivity facilities 642 and interfaces 702, including a wide range of components and systems described throughout this disclosure and in greater detail below. This may include connectivity and interface capabilities for individual services of the platform, for the data handling layers, for the platform as a whole, and / or among value chain entities 652, among others.

[0723] These value chain entities 652 may include any of the wide variety of assets, systems, devices, machines, components, equipment, facilities, individuals or other entities mentioned throughout this disclosure or in the documents incorporated herein by reference, such as, without limitation: machines 724 and their components (e.g., delivery vehicles, forklifts, conveyors, loading machines, cranes, lifts, haulers, trucks, loading machines, unloading machines, packing machines, picking machines, and many others, including robotic systems, e.g., physical robots, collaborative robots (e.g., “cobots”), drones, autonomous vehicles, software bots and many others); products 650 (which may be any category of products, such as a finished goods, software products, hardware products, component products, material, items of equipment, items of consumer packaged goods, consumer products, food products, beverage products, home products, business supply products, consumable products, pharmaceutical products, medical device products, technology products, entertainment products, or any other type of products and / or set of related services); value chain processes 722 (such as shipping processes, hauling processes, maritime processes, inspection processes, hauling processes, loading / unloading processes, packing / unpacking processes, configuration processes, assembly processes, installation processes, quality control processes, environmental control processes (e.g., temperature control, humidity control, pressure control, vibration control, and others), border control processes, port-related processes, software processes (including applications, programs, services, and others), packing and loading processes, financial processes (e.g., insuranceprocesses, reporting processes, transactional processes, and many others), testing and diagnostic processes, security processes, safety processes, reporting processes, asset tracking processes, and many others); wearable and portable devices 720 (such as mobile phones, tablets, dedicated portable devices for value chain applications and processes, data collectors (including mobile data collectors), sensor-based devices, watches, glasses, hearables, head-worn devices, clothing- integrated devices, arm bands, bracelets, neck-worn devices, AR / VR devices, headphones, and many others); workers 718 (such as delivery workers, shipping workers, barge workers, port workers, dock workers, train workers, ship workers, distribution of fulfillment center workers, warehouse workers, vehicle drivers, business managers, engineers, floor managers, demand managers, marketing managers, inventory managers, supply chain managers, cargo handling workers, inspectors, delivery personnel, environmental control managers, financial asset managers, process supervisors and workers (for any of the processes mentioned herein), security personnel, safety personnel and many others); suppliers 642 (such as suppliers of goods and related services of all types, component suppliers, ingredient suppliers, materials suppliers, manufacturers, and many others); customers 662 (including consumers, licensees, businesses, enterprises, value added and other resellers, retailers, end users, distributors, and others who may purchase, license, or otherwise use a category of goods and / or related services); a wide range of operating facilities 712 (such as loading and unloading docks, storage and warehousing facilities 654, vaults, distribution facilities 658 and fulfillment centers 628, air travel facilities 740 (including aircraft, airports, hangars, runways, refueling depots, and the like), maritime facilities 622 (such as port infrastructure facilities 622 (such as docks, yards, cranes, roll-on / roll- off facilities, ramps, containers, container handling systems, waterways 732, locks, and many others), shipyard facilities 638, floating assets 620 (such as ships, barges, boats and others), facilities and other items at points of origin 610 and / or points of destination 628, hauling facilities 710 (such as container ships, barges, and other floating assets 620, as well as land- based vehicles and other delivery systems 632 used for conveying goods, such as trucks, trains, and the like); items or elements factoring in demand (i.e., demand factors 644) (including market factors, events, and many others); items or elements factoring in supply (i.e., supply factors 648)(including market factors, weather, availability of components and materials, and many others); logistics factors 750 (such as availability of travel routes, weather, fuel prices, regulatory factors, availability of space (such as on a vehicle, in a container, in a package, in a warehouse, in a fulfillment center, on a shelf, or the like), and many others); retailers 664 (including online retailers 730 and others such as in the form of eCommerce sites 730); pathways for conveyance (such as waterways 732, roadways 734, air travel routes, railways 738 and the like); robotic systems 744 (including mobile robots, cobots, robotic systems for assisting human workers, robotic delivery systems, and others); drones 748 (including for package delivery, site mapping, monitoring or inspection, and the like); autonomous vehicles 742 (such as for package delivery); software platforms 752 (such as enterprise resource planning platforms, customer relationship management platforms, sales and marketing platforms, asset management platforms, Internet of Things platforms, supply chain management platforms,platform as a service platforms, infrastructure as a service platforms, software-based data storage platforms, analytic platforms, artificial intelligence platforms, and others); and many others. In some example embodiments, the product 1510 may be encompassed as an intelligent product 1510 or the VCNP 604 may include the intelligent product 1510. The intelligent product 1510 may be enabled with a set of capabilities such as, without limitation data processing, networking, sensing, autonomous operation, intelligent agent, natural language processing, speech recognition, voice recognition, touch interfaces, remote control, self-organization, self- healing, process automation, computation, artificial intelligence, analog or digital sensors, cameras, sound processing systems, data storage, data integration, and / or various Internet of Things capabilities, among others. The intelligent product 1510 may include a form of information technology. The intelligent product 1510 may have a processor, computer random access memory, and a communication module. The intelligent product 1510 may be a passive intelligent product that is similar to a RFID type of data structure where the intelligent product may be pinged or read. The product 1510 may be considered a value chain network entity (e.g., under control of platform) and may be rendered intelligent by surrounding infrastructure and adding an RFID such that data may be read from the intelligent product 1510. The intelligent product 1510 may fit in a value chain network in a connected way such that connectivity was built around the intelligent product 1510 through a sensor, an loT device, a tag, or another component.

[0724] In embodiments, the monitoring systems layer 614 may monitor any or all of the value chain entities 652 in a value chain network 668, may exchange data with the value chain entities 652, may provide control instructions to or take instructions from any of the value chain entities 652, or the like, such as through the various capabilities of the data handling layers 608 described throughout this disclosure.NETWORK CHARACTERISTICS OF THE VALUE CHAIN NETWORK ENTITIES

[0725] Referring to FIG. 9, orchestration of a set of deeply interconnected value chain network entities 652 in a value chain network 668 by the value chain network management platform 604 is illustrated. Each of the value chain network entities 652 may have a connection to the VCNP 604, to a set of other value chain network entities 652 (which may be a local network connection, a peer-to-peer connection, a mobile network connection, a connection via a cloud, or other connection), and / or through the VCNP 604 to other value chain network entities 652. The value chain network management platform 604 may manage the connections, configure or provision resources to enable connectivity, and / or manage applications 630 that take advantage of the connections, such as by using information from one set of entities 652 to inform applications 630 involving another set of entities 652, by coordinating activities of a set of entities 652, by providing input to an artificial intelligence system of the VCNP 604 or of or about a set of entities 652, by interacting with edge computation systems deployed on or in entities 652 and their environments, and the like.

[0726] The entities 652 may be external such that the VCNP 604 may interact with these entities 652. When the VCNP 604 functions as the control tower to establish monitoring (e.g., establish monitoring such as common monitoring across several entities 652). In one unified platform, there may be an interface where a user may view various items such as user’s destinations, ports, air and rail assets, as well as orders, etc. Then, the next step may be to establish a common data schema that enables services that work on or in any one of these applications. This may involve taking any of the data that is flowing through or about any of these entities 652 and pull the data into a framework where other applications across supply and demand may interact with the entities 652. This may be a shared data pipeline coming from an loT system and other external data sources, feeding into the monitoring layer, being stored in a common data schema in the storage layer, and then various intelligence may be trained to identify implications across these entities 652. In an example embodiment, a supplier may be bankrupt, or a determination is made that the supplier is bankrupt, and then the VCNP 604 may automatically trigger a substitute smart contract to be sent to a secondary supplier with altered terms. There may be management of different aspects of the supply chain. For example, changing pricing instantly and automatically on the demand side in response to one more supplier’s being identified as bankrupt (e.g., from bankruptcy announcement). Other similar examples may be used based on what occurs in that automation layer which may be enabled by the VCNP 604. Then, at the interface layer of this VCNP 604, a digital twin may be used by user to view all these entities 652 that are not typically shown together and monitor what is going on with each of these entities 652 including identification of problem states. For example, after viewing three quarters of bad financial reports on a supplier, a report may be flagged to watch it closely for potential future bankruptcy, etc.

[0727] For example, an loT system deployed in a fulfillment center 628 may coordinate with an intelligent product 1510 that takes customer feedback about the product 1510, and an application 630 for the fulfillment center 628 may, upon receiving customer feedback via a connection path to the intelligent product 1510 about a problem with the product 1510, initiate a workflow to perform corrective actions on similar products 650 before the products 650 are sent out from the fulfillment center 628. Similarly, a port infrastructure facility 660, such as a yard for holding shipping containers, may inform a fleet of floating assets 620 via connections to the floating assets 620 (such as ships, barges, or the like) that the port is near capacity, thereby kicking off a negotiation process (which may include an automated negotiation based on a set of rules and governed by a smart contract) for the remaining capacity and enabling some assets 620 to be redirected to alternative ports or holding facilities. These and many other connections among value chain network entities 652, whether one-to-one connections, one-to-many connections, many-to-many connections, or connections among defined groups of entities 652 (such as ones controlled by the same owner or operator), are encompassed herein as applications 630 managed by the VCNP 604.VALUE CHAIN NETWORK ACTIVITIES AND APPLICATIONS MANAGED BY THE PLATFORM

[0728] Referring to FIG. 10, the set of applications 614 provided on the VCNP 604, integrated with the VCNP 604 and / or managed by or for the VCNP 604 and / or involving a set of value chain network entities 652 may include, without limitation, one or more of any of a wide range of types of applications, such as: a supply chain management applications 21004 (such as, without limitation, for management of timing, quantities, logistics, shipping, delivery, and other details of orders for goods, components, and other items); an asset management application 814 (such as, without limitation, for managing value chain assets, such as floating assets (such as ships, boats, barges, and floating platforms), real property (such as used for location of warehouses, ports, shipyards, distribution centers and other buildings), equipment, machines and fixtures (such as used for handling containers, cargo, packages, goods, and other items), vehicles (such as forklifts, delivery trucks, autonomous vehicles, and other systems used to move items), human resources (such as workers), software, information technology resources, data processing resources, data storage resources, power generation and / or storage resources, computational resources and other assets); a finance application 822 (such as, without limitation, for handling finance matters relating to value chain entities and assets, such as involving payments, security, collateral, bonds, customs, duties, imposts, taxes and others); a 6 (such as, without limitation, for managing risk or liability with respect to a shipment, goods, a product, an asset, a person, a floating asset, a vehicle, an item of equipment, a component, an information technology system, a security system, a security event, a cybersecurity system, an item of property, a health condition, mortality, fire, flood, weather, disability, negligence, business interruption, injury, damage to property, damage to a business, breach of a contract, and others); a demand management application 824 (such as, without limitation, an application for analyzing, planning, or promoting interest by customers of a category of goods that can be supplied by or with facilities of a value chain product or service, such as a demand planning application, a demand prediction application, a sales application, a future demand aggregation application, a marketing application, an advertising application, an e-commerce application, a marketing analytics application, a customer relationship management application, a search engine optimization application, a sales management application, an advertising network application, a behavioral tracking application, a marketing analytics application, a location-based product or servicetargeting application, a collaborative filtering application, a recommendation engine for a product or service, and others, including ones that use or are enabled by one or more features of an intelligent product 1510 or that are executed using intelligence capabilities on an intelligent product 1510); a trading application 858 (such as, without limitation, a buying application, a selling application, a bidding application, an auction application, a reverse auction application, a bid / ask matching application, an analytic application for analyzing value chain performance, yield, return on investment, or other metrics, or others); a tax application 850 (such as, without limitation, for managing, calculating, reporting, optimizing, or otherwise handling data, events, workflows, or other factors relating to a tax, a tariff, an impost, a levy, a tariff, a duty, a credit, a fee or other government-imposed charge, such as, without limitation, customs duties, valueadded tax, sales tax, income tax, property tax, municipal fees, pollution tax, renewal energy credit, pollution abatement credit, import duties, export duties, and others); an identity management application 830 (such as for managing one or more identities of entities 652 involved in a value chain, such as, without limitation, one or more of an identity verification application, a biometric identify validation application, a pattern-based identity verification application, a location-based identity verification application, a user behavior-based application, a fraud detection application, a network address-based fraud detection application, a black list application, a white list application, a content inspection-based fraud detection application, or other fraud detection application; an inventory management application 820 (such as, without limitation, for managing inventory in a fulfillment center, distribution center, warehouse, storage facility, store, port, ship or other floating asset, or other location); a security application, solution or service 834 (referred to herein as a security application, such as, without limitation, any of the identity management applications 830 noted above, as well as a physical security system (such as for an access control system (such as using biometric access controls, fingerprinting, retinal scanning, passwords, and other access controls), a safe, a vault, a cage, a safe room, a secure storage facility, or the like), a monitoring system (such as using cameras, motion sensors, infrared sensors and other sensors), a perimeter security system, a floating security system for a floating asset, a cyber security system (such as for virus detection and remediation, intrusion detection and remediation, spam detection and remediation, phishing detection and remediation, social engineering detection and remediation, cyber-attack detection and remediation, packet inspection, traffic inspection, DNS attack remediation and detection, and others) or other security application); a safety application 840 (such as, without limitation, for improving safety of workers, for reducing the likelihood of damage to property, for reducing accident risk, for reducing the likelihood of damage to goods (such as cargo), for risk management with respected to insured items, collateral for loans, or the like, including any application for detecting, characterizing or predicting the likelihood and / or scope of an accident or other damaging event, including safety management based on any of the data sources, events or entities noted throughout this disclosure or the documents incorporated herein by reference); a blockchain application 844 (such as, without limitation, a distributed ledger capturing a series of transactions, such as debits or credits, purchases or sales, exchanges of in kind consideration, smart contract events, or the like, or other blockchain-based application); a facility management application 850 (such as, without limitation, for managing infrastructure, buildings, systems, real property, personal property, and other property involved in supporting a value chain, such as a shipyard, a port, a distribution center, a warehouse, a dock, a store, a fulfillment center, a storage facility, or others, as well as for design, management or control of systems and facilities in or around a property, such as an information technology system, a robotic / autonomous vehicle system, a packaging system, a packing system, a picking system, an inventory tracking system, an inspection system, a routing system for mobile robots, a workflow system for human assets, or the like); a regulatory application 852 (such as, without limitation, an application for regulating any of the applications, services, transactions, activities, workflows, events, entities,or other items noted herein and in the documents incorporated by reference herein, such as regulation of permitted routes, permitted cargo and goods, permitted parties to transactions, required disclosures, privacy, pricing, marketing, offering of goods and services, use of data (including data privacy regulations, regulations relating to storage of data and others), banking, marketing, sales, financial planning, and many others); a commerce application, solution or service 854 (such as, without limitation an e-commerce site marketplace, an online site, an auction site or marketplace, a physical goods marketplace, an advertising marketplace, a reverseauction marketplace, an advertising network, or other marketplace); a vendor management application 832 (such as, without limitation, an application for managing a set of vendors or prospective vendors and / or for managing procurement of a set of goods, components or materials that may be supplied in a value chain, such as involving features such as vendor qualification, vendor rating, requests for proposal, requests for information, bonds or other assurances of performance, contract management, and others); an analytics application 838 (such as, without limitation, an analytic application with respect to any of the data types, applications, events, workflows, or entities mentioned throughout this disclosure or the documents incorporated by reference herein, such as a big data application, a user behavior application, a prediction application, a classification application, a dashboard, a pattern recognition application, an econometric application, a financial yield application, a return on investment application, a scenario planning application, a decision support application, a demand prediction application, a demand planning application, a route planning application, a weather prediction application, and many others); a pricing application 842 (such as, without limitation, for pricing of goods, services (including any mentioned throughout this disclosure and the documents incorporated by reference herein; and a smart contract application, solution, or service (referred to collectively herein as a smart contract application 848, such as, without limitation, any of the smart contract types referred to in this disclosure or in the documents incorporated herein by reference, such as a smart contract for sale of goods, a smart contract for an order for goods, a smart contract for a shipping resource, a smart contract for a worker, a smart contract for delivery of goods, a smart contract for installation of goods, a smart contract using a token or cryptocurrency for consideration, a smart contract that vests a right, an option, a future, or an interest based on a future condition, a smart contract for a security, commodity, future, option, derivative, or the like, a smart contract for current or future resources, a smart contract that is configured to account for or accommodate a tax, regulatory or compliance parameter, a smart contract that is configured to execute an arbitrage transaction, or many others). Thus, the value chain management platform 604 may host an enable interaction among a wide range of disparate applications 630 (such term including the above-referenced and other value chain applications, services, solutions, and the like), such that by virtue of shared microservices, shared data infrastructure, and shared intelligence, any pair or larger combination or permutation of such services may be improved relative to an isolated application of the same type.

[0729] Referring still to FIG. 10, the set of applications 614 provided on the VCNP 604, integrated with the VCNP 604 and / or managed by or for the VCNP 604 and / or involving a set of value chain network entities 652 may further include, without limitation: a payments application 860 (such as for calculating payments (including based on situational factors such as applicable taxes, duties and the like for the geography of an entity 652), transferring funds, resolving payments to parties, and the like, for any of the applications 630 noted herein); a process management application 862 (such as for managing any of the processes or workflows described throughout this disclosure, including supply processes, demand processes, logistics processes, delivery processes, fulfillment processes, distribution processes, ordering processes, navigation processes, and many others); a compatibility testing application 864, such as for assessing compatibility among value chain network entities 652 or activities involved in any of the processes, workflows, activities, or other applications 630 described herein (such as for determining compatibility of a container or package with a product 1510, the compatibility of a product 1510 with a set of customer requirements, the compatibility of a product 1510 with another product 1510 (such as where one is a refill, resupply, replacement part, or the like for the other), the compatibility of a infrastructure and equipment entities 652 (such as between a container ship or barge and a port or waterway, between a container and a storage facility, between a truck and a roadway, between a drone or robot and a package, between a drone, AV or robot and a delivery destination, and many others); an infrastructure testing application 802 (such as for testing the capabilities of infrastructure elements to support a product 1510 or an application 630 (such as, without limitation, storage capabilities, lifting capabilities, moving capabilities, storage capacity, network capabilities, environmental control capabilities, software capabilities, security capabilities, and many others)); and / or an incident management application 910 (such as for managing events, accidents, and other incidents that may occur in one or more environments involving value chain network entities 652, such as, without limitation, vehicle accidents, worker injuries, shutdown incidents, property damage incidents, product damage incidents, product liability incidents, regulatory non-compliance incidents, health and / or safety incidents, traffic congestion and / or delay incidents (including network traffic, data traffic, vehicle traffic, maritime traffic, human worker traffic, and others, as well as combinations among them), product failure incidents, system failure incidents, system performance incidents, fraud incidents, misuse incidents, unauthorized use incidents, and many others).

[0730] Referring still to FIG. 10, the set of applications 614 provided on the VCNP 604, integrated with the VCNP 604 and / or managed by or for the VCNP 604 and / or involving a set of value chain network entities 652 may further include, without limitation: a predictive maintenance application 910 (such as for anticipating, predicting, and undertaking actions to manage faults, failures, shutdowns, damage, required maintenance, required repairs, required service, required support, or the like for a set of value chain network entities 652, such as products 650, equipment, infrastructure, buildings, vehicles, and others); a logistics application 912 (such as for managing logistics for pickups, deliveries, transfer of goods onto hauling facilities, loading, unloading, packing, picking, shipping, driving, and other activities involvingin the scheduling and management of the movement of products 650 and other items between points of origin and points of destination through various intermediate locations; a reverse logistic application 914 (such as for handling logistics for returned products 650, waste products, damaged goods, or other items that can be transferred on a return logistics path); a waste reduction application 920 (such as for reducing packaging waste, solid waste, waste of energy, liquid waste, pollution, contaminants, waste of computing resources, waste of human resources, or other waste involving a value chain network entity 652 or activity); an augmented reality, mixed reality and / or virtual reality application 930 (such as for visualizing one or more value chain network entities 652 or activities involved in one or more of the applications 630, such as, without limitation, movement of a product 1510, the interior of a facility, the status or condition of an item of goods, one or more environmental conditions, a weather condition, a packing configuration for a container or a set of containers, or many others); a demand prediction application 940 (such as for predicting demand for a product 1510, a category of products, a potential product, and / or a factor involved in demand, such as a market factor, a wealth factor, a demographic factor, a weather factor, an economic factor, or the like); a demand aggregation application 942 (such as for aggregating information, orders and / or commitments (optionally embodied in one or more contracts, which may be smart contracts) for one or more products 650, categories, or the like, including current demand for existing products and future demand for products that are not yet available); a customer profiling application 944 (such as for profiling one or more demographic, psychographic, behavioral, economic, geographic, or other attributes of a set of customers, including based on historical purchasing data, loyalty program data, behavioral tracking data (including data captured in interactions by a customer with a smart product 1510), online clickstream data, interactions with intelligent agents, and other data sources); and / or a component supply application 948 (such as for managing a supply chain of components for a set of products 650).

[0731] Referring still to FIG. 10, the set of applications 614 provided on the VCNP 604, integrated with the VCNP 604 and / or managed by or for the VCNP 604 and / or involving a set of value chain network entities 652 may further include, without limitation: a policy management application 868 (such as for deploying one or more policies, rules, or the like for governance of one or more value chain network entities 652 or applications 630, such as to govern execution of one or more workflows (which may involve configuring polices in the platform 604 on a per- workflow basis), to govern compliance with regulations (including maritime, food and drug, medical, environmental, health, safety, tax, financial reporting, commercial, and other regulations as described throughout this disclosure or as would be understood in the art), to govern provisioning of resources (such as connectivity, computing, human, energy, and other resources), to govern compliance with corporate policies, to govern compliance with contracts (including smart contracts, wherein the platform 604 may automatically deploy governance features to relevant entities 652 and applications 630, such as via connectivity facilities 642), to govern interactions with other entities (such as involving policies for sharing of information and access to resources), to govern data access (including privacy data, operational data, status data,and many other data types), to govern security access to infrastructure, products, equipment, locations, or the like, and many others; a product configuration application 870 (such as for allowing a product manager and / or automated product configuration process (optionally using robotic process automation) to determine a configuration for a product 1510, including configuration on-the-fly, such as during agile manufacturing, or involving configuration or customization in route (such as by 3D printing one or more features or elements), or involving configuration or customization remotely, such as by downloading firmware, configuring field programmable gate arrays, installing software, or the like; a warehousing and fulfillment application 872 (such as for managing a warehouse, distribution center, fulfillment center, or the like, such as involving selection of products, configuring storage locations for products, determining routes by which personnel, mobile robots, and the like move products around a facility, determining picking and packing schedules, routes and workflows, managing operations of robots, drones, conveyors, and other facilities, determining schedules for moving products out to loading docks or the like, and many other functions); a kit configuration and deployment application 874 (such as for enabling a user of the VCNP to configure a kit, box, or otherwise pre-integrated, pre-provisioned, and / or pre-configured system to allow a customer or worker to rapidly deploy a subset of capabilities of the VCNP 604 for a specific value chain network entity 652 and / or application 630); and / or a product testing application 878 for testing a product 1510 (including testing for performance, activation of capabilities and features, safety, compliance with policy or regulations, quality, quality of service, likelihood of failure, and many other factors).

[0732] Referring still to FIG. 10, the set of applications 614 provided on the VCNP 604, integrated with the VCNP 604 and / or managed by or for the VCNP 604 and / or involving a set of value chain network entities 652 may further include, without limitation a maritime fleet management application 880 (for managing a set of maritime assets, such as container ships, barges, boats, and the like, as well as related infrastructure facilities such as docks, cranes, ports, and others, such as to determine optimal routes for fleet assets based on weather, market, traffic, and other conditions, to ensure compliance with policies and regulations, to ensure safety, to improve environmental factors, to improve financial metrics, and many others); a shipping management application 882 (such as for managing a set of shipping assets, such as trucks, trains, airplanes, and the like, such as to optimize financial yield, to improve safety, to reduce energy consumption, to reduce delays, to mitigate environmental impact, and for many other purposes); an opportunity matching application 884 (such as for matching one or more demand factors with one or more supply factors, for matching needs and capabilities of value chain network entities 652, for identifying reverse logistics opportunities, for identifying opportunities for inputs to enrich analytics, artificial intelligence and / or automation, for identifying costsaving opportunities, for identifying profit and / or arbitrage opportunities, and many others); a workforce management application 888 (such as for managing workers in various work forces, including work forces in, on or for fulfillment centers, ships, ports, warehouses, distribution centers, enterprise management locations, retail stores, online / ecommerce site managementfacilities, ports, ships, boats, barges, trains, depots, and other facilities mentioned throughout this disclosure); a distribution and delivery application 890 (such as for planning, scheduling, routing, and otherwise managing distribution and delivery of products 650 and other items); and / or an enterprise resource planning (ERP) application 892 (such as for planning utilization of enterprise resources, including workforce resources, financial resources, energy resources, physical assets, digital assets, and other resources).CORE CAPABILITIES AND INTERACTIONS OF THE DATA HANDLING LAYERS (ADAPTIVE INTELLIGENCE, MONITORING, DATA STORAGE AND APPLICATIONS)

[0733] Referring to FIG. 11, a high-level schematic of an embodiment of the value chain network management platform 604 is illustrated, including a set of systems, applications, processes, modules, services, layers, devices, components, machines, products, sub-systems, interfaces, connections, and other elements working in coordination to enable intelligent management of sets of the value chain entities 652 that may occur, operate, transact or the like within, or own, operate, support or enable, one or more value chain network processes, workflows, activities, events and / or applications 630 or that may otherwise be part of, integrated with, linked to, or operated on by the platform 604 in connection with a product 1510 (which may be a finished good, software product, hardware product, component product, material, item of equipment, consumer packaged good, consumer product, food product, beverage product, home product, business supply product, consumable product, pharmaceutical product, medical device product, technology product, entertainment product, or any other type of product or related service, which may, in embodiments, encompass an intelligent product that is enabled with processing, networking, sensing, computation, and / or other Internet of Things capabilities). Value chain entities 652, such as involved in or for a wide range of value chain activities (such as supply chain activities, logistics activities, demand management and planning activities, delivery activities, shipping activities, warehousing activities, distribution and fulfillment activities, inventory aggregation, storage and management activities, marketing activities, and many others, as involved in various value chain network processes, workflows, activities, events and applications 630 may include any of the wide variety of assets, systems, devices, machines, components, equipment, facilities, individuals or other entities mentioned throughout this disclosure or in the documents incorporated herein by reference.

[0734] In embodiments, the value chain network management platform 604 may include the set of data handling layers 608, each of which is configured to provide a set of capabilities that facilitate development and deployment of intelligence, such as for facilitating automation, machine learning, applications of artificial intelligence, intelligent transactions, intelligent operations, remote control, analytics, monitoring, reporting, state management, event management, process management, and many others, for a wide variety of value chain network applications and end uses. In embodiments, the data handling layers 608 may include a value chain network monitoring systems layer 614, a value chain network entity- oriented data storage systems layer 624 (referred to in some cases herein for convenience simply as a data storage layer 624), an adaptive intelligent systems layer 614 and a value chain network managementplatform 604. The value chain network management platform 604 may include the data handling layers 608 such that the value chain network management platform 604 may provide management of the value chain network management platform 604 and / or management of the other layers such as the value chain network monitoring systems layer 614, the value chain network entity-oriented data storage systems layer 624 (e.g., data storage layer 624), and the adaptive intelligent systems layer 614. Each of the data handling layers 608 may include a variety of services, programs, applications, workflows, systems, components and modules, as further described herein and in the documents incorporated herein by reference. In embodiments, each of the data handling layers 608 (and optionally the platform 604 as a whole) is configured such that one or more of its elements can be accessed as a service by other layers 624 or by other systems (e.g., being configured as a platform-as-a-service deployed on a set of cloud infrastructure components in a microservices architecture). For example, the platform 604 may have (or may configure and / or provision), and a data handling layer 608 may use, a set of connectivity facilities 642, such as network connections (including various configurations, types and protocols), interfaces, ports, application programming interfaces (APIs), brokers, services, connectors, wired or wireless communication links, human-accessible interfaces, software interfaces, micro-services, SaaS interfaces, PaaS interfaces, laaS interfaces, cloud capabilities, or the like by which data or information may be exchanged between a data handling layer 608 and other layers, systems or sub-systems of the platform 604, as well as with other systems, such as value chain entities 652 or external systems, such as cloud-based or on-premises enterprise systems (e.g., accounting systems, resource management systems, CRM systems, supply chain management systems and many others). Each of the data handling layers 608 may include a set of services (e.g., microservices), for data handling, including facilities for data extraction, transformation and loading; data cleansing and deduplication facilities; data normalization facilities; data synchronization facilities; data security facilities; computational facilities (e.g., for performing pre-defined calculation operations on data streams and providing an output stream); compression and de-compression facilities; analytic facilities (such as providing automated production of data visualizations) and others.

[0735] In embodiments, each data handling layer 608 has a set of application programming connectivity facilities 642 for automating data exchange with each of the other data handling layers 608. These may include data integration capabilities, such as for extracting, transforming, loading, normalizing, compression, decompressing, encoding, decoding, and otherwise processing data packets, signals, and other information as it exchanged among the layers and / or the applications 630, such as transforming data from one format or protocol to another as needed in order for one layer to consume output from another. In embodiments, the data handling layers 608 are configured in a topology that facilitates shared data collection and distribution across multiple applications and uses within the platform 604 by the value chain monitoring systems layer 614. The value chain monitoring systems layer 614 may include, integrate with, and / or cooperate with various data collection and management systems 640, referred to for convenience in some cases as data collection systems 640, for collecting and organizing data collected fromor about value chain entities 652, as well as data collected from or about the various data layers 624 or services or components thereof. For example, a stream of physiological data from a wearable device worn by a worker undertaking a task or a consumer engaged in an activity can be distributed via the monitoring systems layer 614 to multiple distinct applications in the value chain management platform 604, such as one that facilitates monitoring the physiological, psychological, performance level, attention, or other state of a worker and another that facilitates operational efficiency and / or effectiveness. In embodiments, the monitoring systems layer 614 facilitates alignment, such as time-synchronization, normalization, or the like of data that is collected with respect to one or more value chain network entities 652. For example, one or more video streams or other sensor data collected of or with respect to a worker 718 or other entity in a value chain network facility or environment, such as from a set of camera-enabled loT devices, may be aligned with a common clock, so that the relative timing of a set of videos or other data can be understood by systems that may process the videos, such as machine learning systems that operate on images in the videos, on changes between images in different frames of the video, or the like. In such an example, the monitoring systems layer 614 may further align a set of videos, camera images, sensor data, or the like, with other data, such as a stream of data from wearable devices, a stream of data produced by value chain network systems (such as ships, lifts, vehicles, containers, cargo handling systems, packing systems, delivery systems, drones / robots, and the like), a stream of data collected by mobile data collectors, and the like. Configuration of the monitoring systems layer 614 as a common platform, or set of microservices, that are accessed across many applications, may dramatically reduce the number of interconnections required by an owner or other operator within a value chain network in order to have a growing set of applications monitoring a growing set of loT devices and other systems and devices that are under its control.

[0736] In embodiments, the data handling layers 608 are configured in a topology that facilitates shared or common data storage across multiple applications and uses of the platform 604 by the value chain network-oriented data storage systems layer 624, referred to herein for convenience in some cases simply as the data storage layer 624 or storage layer 624. For example, various data collected about the value chain entities 652, as well as data produced by the other data handling layers 608, may be stored in the data storage layer 624, such that any of the services, applications, programs, or the like of the various data handling layers 608 can access a common data source (which may comprise a single logical data source that is distributed across disparate physical and / or virtual storage locations). This may facilitate a dramatic reduction in the amount of data storage required to handle the enormous amount of data produced by or about value chain network entities 652 as applications 630 and uses of value chain networks grow and proliferate. For example, a supply chain or inventory management application in the value chain management platform 604, such as one for ordering replacement parts for a machine or item of equipment, may access the same data set about what parts have been replaced for a set of machines as a predictive maintenance application that is used topredict whether a component of a ship, or facility of a port is likely to require replacement parts. Similarly, prediction may be used with respect to the resupply of items.

[0737] In embodiments, value chain network data objects 1004 may be provided according to an object-oriented data model that defines classes, objects, attributes, parameters and other features of the set of data objects (such as associated with value chain network entities 652 and applications 630) that are handled by the platform 604.

[0738] In embodiments, the data storage systems layer 624 may provide an extremely rich environment for collection of data that can be used for extraction of features or inputs for intelligence systems, such as expert systems, analytic systems, artificial intelligence systems, robotic process automation systems, machine learning systems, deep learning systems, supervised learning systems, or other intelligent systems as disclosed throughout this disclosure and the documents incorporated herein by reference. As a result, each application 630 in the platform 604 and each adaptive intelligent system in the adaptive intelligent systems layer 614 can benefit from the data collected or produced by or for each of the others. In embodiments, the data storage systems layer 624 may facilitate collection of data that can be used for extraction of features or inputs for intelligence systems such as a development framework from artificial intelligence. In examples, the collections of data may pull in and / or house event logs (naturally stored or ad-hoc, as needed), perform periodic checks on onboard diagnostic data, or the like. In examples, pre calculation of features may be deployed using AWS Lambda, for example, or various other cloud-based on-demand compute capabilities, such as pre-calculations, multiplexing signals. In many examples, there are pairings (doubles, triples, quadruplets, etc.) of similar kinds of value chain entities that may use one or more sets of capabilities of the data handling layers 608 to deploy connectivity and services across value chain entities and across applications used by the entities even when amassing hundreds and hundreds of data types from relatively disparate entities. In these examples, various pairings of similar types of value chain entities using, at least in part, the connectivity and services across value chain entities and applications, may direct the information from the pairings of connected data to artificial intelligence services including the various neural networks disclosed herein and hybrid combinations thereof. In these examples, genetic programming techniques may be deployed to prune some of the input features in the information from the pairings of connected data. In these examples, genetic programming techniques may also be deployed to add to and augment the input features in the information from the pairings. These genetic programming techniques may be shown to increase the efficacy of the determinations established by the artificial intelligence services. In these examples, the information from the pairings of connected data may be migrated to other layers on the platform including to support or deploy robotic process automation, prediction, forecasting, and other resources such that the shared data schema may facilitate as capabilities and resources for the platform 604.

[0739] A wide range of data types may be stored in the storage layer 624 using various storage media and data storage types, data architectures 1002, and formats, including, without limitation: asset and facility data 1030, state data 1140 (such as indicating a state, conditionstatus, or other indicator with respect to any of the value chain network entities 652, any of the applications 630 or components or workflows thereof, or any of the components or elements of the platform 604, among others), worker data 1032 (including identity data, role data, task data, workflow data, health data, attention data, mood data, stress data, physiological data, performance data, quality data and many other types); event data 1034 ((such as with respect to any of a wide range of events, including operational data, transactional data, workflow data, maintenance data, and many other types of data that includes or relates to events that occur within a value chain network 668 or with respect to one or more applications 630, including process events, financial events, transaction events, output events, input events, state-change events, operating events, workflow events, repair events, maintenance events, service events, damage events, injury events, replacement events, refueling events, recharging events, shipping events, warehousing events, transfers of goods, crossing of borders, moving of cargo, inspection events, supply events, and many others); claims data 664 (such as relating to insurance claims, such as for business interruption insurance, product liability insurance, insurance on goods, facilities, or equipment, flood insurance, insurance for contract-related risks, and many others, as well as claims data relating to product liability, general liability, workers compensation, injury and other liability claims and claims data relating to contracts, such as supply contract performance claims, product delivery requirements, warranty claims, indemnification claims, delivery requirements, timing requirements, milestones, key performance indicators and others); accounting data 730 (such as data relating to completion of contract requirements, satisfaction of bonds, payment of duties and tariffs, and others); and risk management data 732 (such as relating to items supplied, amounts, pricing, delivery, sources, routes, customs information and many others), among many other data types associated with value chain network entities 652 and applications 630.

[0740] In embodiments, the data handling layers 608 are configured in a topology that facilitates shared adaptation capabilities, which may be provided, managed, mediated and the like by one or more of a set of services, components, programs, systems, or capabilities of the adaptive intelligent systems layer 614, referred to in some cases herein for convenience as the adaptive intelligence layer 614. The adaptive intelligence systems layer 614 may include a set of data processing, artificial intelligence and computational systems 634 that are described in more detail elsewhere throughout this disclosure. Thus, use of various resources, such as computing resources (such as available processing cores, available servers, available edge computing resources, available on-device resources (for single devices or peered networks), and available cloud infrastructure, among others), data storage resources (including local storage on devices, storage resources in or on value chain entities or environments (including on-device storage, storage on asset tags, local area network storage and the like), network storage resources, cloudbased storage resources, database resources and others), networking resources (including cellular network spectrum, wireless network resources, fixed network resources and others), energy resources (such as available battery power, available renewable energy, fuel, grid-based power, and many others) and others may be optimized in a coordinated or shared way on behalfof an operator, enterprise, or the like, such as for the benefit of multiple applications, programs, workflows, or the like. For example, the adaptive intelligence layer 614 may manage and provision available network resources for both a supply chain management application and for a demand planning application (among many other possibilities), such that low latency resources are used for supply chain management application (where rapid decisions may be important) and longer latency resources are used for the demand planning application. As described in more detail throughout this disclosure and the documents incorporated herein by reference, a wide variety of adaptations may be provided on behalf of the various services and capabilities across the various layers 624, including ones based on application requirements, quality of service, on- time delivery, service objectives, budgets, costs, pricing, risk factors, operational objectives, efficiency objectives, optimization parameters, returns on investment, profitability, uptime / downtime, worker utilization, and many others.

[0741] The value chain management platform 604, referred to in some cases herein for convenience as the platform 604, may include, integrate with, and enable the various value chain network processes, workflows, activities, events and applications 630 described throughout this disclosure that enable an operator to manage more than one aspect of a value chain network environment or entity 652 in a common application environment (e.g., shared, pooled, similarly licenses whether shared data for one person, multiple people, or anonymized), such as one that takes advantage of common data storage in the data storage layer 624, common data collection or monitoring in the monitoring systems layer 614 and / or common adaptive intelligence of the adaptive intelligence layer 614. Outputs from the applications 630 in the platform 604 may be provided to the other data handing layers 624. These may include, without limitation, state and status information for various objects, entities, processes, flows and the like; object information, such as identity, attribute and parameter information for various classes of objects of various data types; event and change information, such as for workflows, dynamic systems, processes, procedures, protocols, algorithms, and other flows, including timing information; outcome information, such as indications of success and failure, indications of process or milestone completion, indications of correct or incorrect predictions, indications of correct or incorrect labeling or classification, and success metrics (including relating to yield, engagement, return on investment, profitability, efficiency, timeliness, quality of service, quality of product, customer satisfaction, and others) among others. Outputs from each application 630 can be stored in the data storage layer 624, distributed for processing by the data collection layer 614, and used by the adaptive intelligence layer 614. The cross-application nature of the platform 604 thus facilitates convenient organization of all of the necessary infrastructure elements for adding intelligence to any given application, such as by supplying machine learning on outcomes across applications, providing enrichment of automation of a given application via machine learning based on outcomes from other applications or other elements of the platform 604, and allowing application developers to focus on application-native processes while benefiting from other capabilities of the platform 604. In examples, there may be systems, components, services and other capabilities that optimize control, automation, or one or more performance characteristicsof one or more value chain network entities 652; or ones that may generally improve any of process and application outputs and outcomes 1040 pursued by use of the platform 604. In some examples, outputs and outcomes 1040 from various applications 630 may be used to facilitate automated learning and improvement of classification, prediction, or the like that is involved in a step of a process that is intended to be automated.SOME DATA STORAGE LAYER DETAILS - ALTERNATIVE DATA ARCHITECTURES

[0742] Referring to FIG. 12, additional details, components, sub-systems, and other elements of an optional embodiment of the data storage layer 624 of the platform 604 are illustrated. Various data architectures may be used, including conventional relational and object-oriented data architectures, blockchain architectures 1180, asset tag data storage architectures 1178, local storage architectures 1190, network storage architectures 1174, multi -tenant architectures 1132, distributed data architectures 1002, value chain network (VCN) data object architectures 1004, cluster-based architectures 1128, event data-based architectures 1034, state data-based architectures 1140, graph database architectures 1124, self-organizing architectures 1134, and other data architectures 1002.

[0743] The adaptive intelligent systems layer 614 of the platform 604 may include one or more protocol adaptors 1110 for facilitating data storage, retrieval access, query management, loading, extraction, normalization, and / or transformation to enable use of the various other data storage architectures 1002, such as allowing extraction from one form of database and loading to a data system that uses a different protocol or data structure.

[0744] In embodiments, the value chain network-oriented data storage systems layer 624 may include, without limitation, physical storage systems, virtual storage systems, local storage systems (e.g., part of the local storage architectures 1190), distributed storage systems, databases, memory, network-based storage, network-attached storage systems (e.g., part of the network storage architectures 1174such as using NVME, storage attached networks, and other network storage systems), and many others.

[0745] In embodiments, the storage layer 624 may store data in one or more knowledge graphs (such as a directed acyclic graph, a data map, a data hierarchy, a data cluster including links and nodes, a self-organizing map, or the like) in the graph database architectures 1124. In example embodiments, the knowledge graph may be a prevalent example of when a graph database and graph database architecture may be used. In some examples, the knowledge graph may be used to graph a workflow. For a linear workflow, a directed acyclic graph may be used. For a contingent workflow, a cyclic graph may be used. The graph database (e.g., graph database architectures 1124) may include the knowledge graph or the knowledge graph may be an example of the graph database. In example embodiments, the knowledge graph may include ontology and connections (e.g., relationships) between the ontology of the knowledge graph. In an example, the knowledge graph may be used to capture an articulation of knowledge domains of a human expert such that there may be an identification of opportunities to design and build robotic process automation or other intelligence that may replicate this knowledge set. Theplatform may be used to recognize that a type of expert is using this factual knowledge base (from the knowledge graph) coupled with competencies that may be replicable by artificial intelligence that may be different depending on type of expertise involved. For example, artificial intelligence such as a convolutional neural network may be used with spatiotemporal aspects that may be used to diagnose issues or packing up a box in a warehouse. Whereas the platform may use a different type of knowledge graph for a self-organizing map of an expert whose main job is to segment customers into customer segmentation groups. In some examples, the knowledge graph may be built from various data such as job credentials, job listings, parsing output deliverables. In embodiments, the data storage layer 624 may store data in a digital thread, ledger, or the like, such as for maintaining a serial or other records of an entities 652 over time, including any of the entities described herein. In embodiments, the data storage layer 624 may use and enable an asset tag 1178, which may include a data structure that is associated with an asset and accessible and managed, such as by use of access controls, so that storage and retrieval of data is optionally linked to local processes, but also optionally open to remote retrieval and storage options. In embodiments, the storage layer 624 may include one or more blockchains 1180, such as ones that store identity data, transaction data, historical interaction data, and the like, such as with access control that may be role-based or may be based on credentials associated with a value chain entity 652, a service, or one or more applications 630. Data stored by the data storage systems 624 may include accounting and other financial data 730, access data 734, asset and facility data 1030 (such as for any of the value chain assets and facilities described herein), asset tag data 1178, worker data 1032, event data 1034, risk management data 732, pricing data 738, safety data 664 and many other types of data that may be associated with, produced by, or produced about any of the value chain entities and activities described herein and in the documents incorporated by reference.ADAPTIVE INTELLIGENT SYSTEMS AND MONITORING LAYERS

[0746] Referring to FIG. 13, additional details, components, sub-systems, and other elements of an optional embodiment of the platform 604 are illustrated. The management platform 604 may, in various optional embodiments, include the set of applications 614, by which an operator or owner of a value chain network entity, or other users, may manage, monitor, control, analyze, or otherwise interact with one or more elements of a value chain network entity 652, such as any of the elements noted in connection above and throughout this disclosure.

[0747] In embodiments, the adaptive intelligent systems layer 614 may include a set of systems, components, services and other capabilities that collectively facilitate the coordinated development and deployment of intelligent systems, such as ones that can enhance one or more of the applications 630 at the application platform 604; ones that can improve the performance of one or more of the components, or the overall performance (e.g., speed / latency, reliability, quality of service, cost reduction, or other factors) of the connectivity facilities 642; ones that can improve other capabilities within the adaptive intelligent systems layer 614; ones that improve the performance (e.g., speed / latency, energy utilization, storage capacity, storageefficiency, reliability, security, or the like) of one or more of the components, or the overall performance, of the value chain network-oriented data storage systems 624; ones that optimize control, automation, or one or more performance characteristics of one or more value chain network entities 652; or ones that generally improve any of the process and application outputs and outcomes 1040 pursued by use of the platform 604.

[0748] These adaptive intelligent systems 614 may include a robotic process automation system 1442, a set of protocol adaptors 1110, a packet acceleration system 1410, an edge intelligence system 1420 (which may be a self-adaptive system), an adaptive networking system 1430, a set of state and event managers 1450, a set of opportunity miners 1460, a set of artificial intelligence systems 1160, a set of digital twin systems 1700, a set of entity interaction systems 1920 (such as for setting up, provisioning, configuring and otherwise managing sets of interactions between and among sets of value chain network entities 652 in the value chain network 668), and other systems.

[0749] In embodiments, the value chain monitoring systems layer 614 and its data collection systems 640 may include a wide range of systems for the collection of data. This layer may include, without limitation, real time monitoring systems 1520 (such as onboard monitoring systems like event and status reporting systems on ships and other floating assets, on delivery vehicles, on trucks and other hauling assets, and in shipyards, ports, warehouses, distribution centers and other locations; on-board diagnostic (OBD) and telematics systems on floating assets, vehicles and equipment; systems providing diagnostic codes and events via an event bus, communication port, or other communication system; monitoring infrastructure (such as cameras, motion sensors, beacons, RFID systems, smart lighting systems, asset tracking systems, person tracking systems, and ambient sensing systems located in various environments where value chain activities and other events take place), as well as removable and replaceable monitoring systems, such as portable and mobile data collectors, RFID and other tag readers, smart phones, tablets and other mobile devices that are capable of data collection and the like); software interaction observation systems 1500 (such as for logging and tracking events involved in interactions of users with software user interfaces, such as mouse movements, touchpad interactions, mouse clicks, cursor movements, keyboard interactions, navigation actions, eye movements, finger movements, gestures, menu selections, and many others, as well as software interactions that occur as a result of other programs, such as over APIs, among many others); mobile data collectors 1170 (such as described extensively herein and in documents incorporated by reference), visual monitoring systems 1930 (such as using video and still imaging systems, LIDAR, IR and other systems that allow visualization of items, people, materials, components, machines, equipment, personnel, gestures, expressions, positions, locations, configurations, and other factors or parameters of entities 652, as well as inspection systems that monitor processes, activities of workers and the like); point of interaction systems 1530 (such as dashboards, user interfaces, and control systems for value chain entities); physical process observation systems 1510 (such as for tracking physical activities of operators, workers, customers, or the like, physical activities of individuals (such as shippers, delivery workers,packers, pickers, assembly personnel, customers, merchants, vendors, distributors and others), physical interactions of workers with other workers, interactions of workers with physical entities like machines and equipment, and interactions of physical entities with other physical entities, including, without limitation, by use of video and still image cameras, motion sensing systems (such as including optical sensors, LIDAR, IR and other sensor sets), robotic motion tracking systems (such as tracking movements of systems attached to a human or a physical entity) and many others; machine state monitoring systems 1940 (including onboard monitors and external monitors of conditions, states, operating parameters, or other measures of the condition of any value chain entity, such as a machine or component thereof, such as a machine, such as a client, a server, a cloud resource, a control system, a display screen, a sensor, a camera, a vehicle, a robot, or other machine); sensors and cameras 1950 and other loT data collection systems 1172 (including onboard sensors, sensors or other data collectors (including click tracking sensors) in or about a value chain environment (such as, without limitation, a point of origin, a loading or unloading dock, a vehicle or floating asset used to convey goods, a container, a port, a distribution center, a storage facility, a warehouse, a delivery vehicle, and a point of destination), cameras for monitoring an entire environment, dedicated cameras for a particular machine, process, worker, or the like, wearable cameras, portable cameras, cameras disposed on mobile robots, cameras of portable devices like smart phones and tablets, and many others, including any of the many sensor types disclosed throughout this disclosure or in the documents incorporated herein by reference); indoor location monitoring systems 1532 (including cameras, IR systems, motion-detection systems, beacons, RFID readers, smart lighting systems, triangulation systems, RF and other spectrum detection systems, time-of-flight systems, chemical noses and other chemical sensor sets, as well as other sensors); user feedback systems 1534 (including survey systems, touch pads, voice-based feedback systems, rating systems, expression monitoring systems, affect monitoring systems, gesture monitoring systems, and others); behavioral monitoring systems 1538 (such as for monitoring movements, shopping behavior, buying behavior, clicking behavior, behavior indicating fraud or deception, user interface interactions, product return behavior, behavior indicative of interest, attention, boredom or the like, mood-indicating behavior (such as fidgeting, staying still, moving closer, or changing posture) and many others); and any of a wide variety of Internet of Things (loT) data collectors 1172, such as those described throughout this disclosure and in the documents incorporated by reference herein.

[0750] In embodiments, the value chain monitoring systems layer 614 and its data collection systems 640 may include an entity discovery system 1900 for discovering one or more value chain network entities 652, such as any of the entities described throughout this disclosure. This may include components or sub-systems for searching for entities within the value chain network 668, such as by device identifier, by network location, by geolocation (such as by geofence), by indoor location (such as by proximity to known resources, such as loT-enabled devices and infrastructure, Wifi routers, switches, or the like), by cellular location (such as by proximity to cellular towers), by identity management systems (such as where an entity 652 isassociated with another entity 652, such as an owner, operator, user, or enterprise by an identifier that is assigned by and / or managed by the platform 604), and the like. Entity discovery 1900 may initiate a handshake among a set of devices, such as to initiate interactions that serve various applications 630 or other capabilities of the platform 604.

[0751] Referring to FIG. 14, a management platform of an information technology system, such as a management platform for a value chain of goods and / or services is depicted as a block diagram of functional elements and representative interconnections. The management platform includes a user interface 3020 that provides, among other things, a set of adaptive intelligence systems 614. The adaptive intelligence systems 614 provide coordinated intelligence (including artificial intelligence 1160, expert systems 3002, machine learning 3004, and the like) for a set of demand management applications 824 and for a set of supply chain applications 812 for a category of goods 3010, which may be produced and sold through the value chain. The adaptive intelligence systems 614 may deliver artificial intelligence 1160 through a set of data processing, artificial intelligence and computational systems 634. In embodiments, the adaptive intelligence systems 614 are selectable and / or configurable through the user interface 3020 so that one or more of the adaptive intelligence systems 614 can operate on or in cooperation with the sets of value chain applications (e.g., demand management applications 824 and supply chain applications 812). The adaptive intelligence systems 614 may include artificial intelligence, including any of the various expert systems, artificial intelligence systems, neural networks, supervised learning systems, machine learning systems, deep learning systems, and other systems described throughout this disclosure and in the documents incorporated by reference.

[0752] In embodiments, user interface may include interfaces for configuring an artificial intelligence system 1160 to take inputs from selected data sources of the value chain (such as data sources used by the set of demand management applications 824 and / or the set of supply chain applications 812) and supply them, such as to a neural network, artificial intelligence system 1160 or any of the other adaptive intelligence systems 614 described throughout this disclosure and in the documents incorporated herein by reference to enhance, control, improve, optimize, configure, adapt or have another impact on a value chain for the category of goods 3010. In embodiments, the selected data sources of the value chain may be applied either as inputs for classification or prediction, or as outcomes relating to the value chain, the category of goods 3010 and the like.

[0753] In embodiments, providing coordinated intelligence may include providing artificial intelligence capabilities, such as artificial intelligence systems 1160 and the like. Artificial intelligence systems may facilitate coordinated intelligence for the set of demand management applications 824 or the set of supply chain applications 812 or both, such as for a category of goods, such as by processing data that is available in any of the data sources of the value chain, such as value chain processes, bills of materials, manifests, delivery schedules, weather data, traffic data, goods design specifications, customer complaint logs, customer reviews, Enterprise Resource Planning (ERP) System, Customer Relationship Management (CRM) System,Customer Experience Management (CEM) System, Service Lifecycle Management (SLM) System, Product Lifecycle Management (PLM) System, and the like.

[0754] In embodiments, the user interface 3020 may provide access to, among other things artificial intelligence capabilities, applications, systems and the like for coordinating intelligence for applications of the value chain and particularly for value chain applications for the category of goods 3010. The user interface 3020 may be adapted to receive information descriptive of the category of goods 3010 and configure user access to the artificial intelligence capabilities responsive thereto, so that the user, through the user interface is guided to artificial intelligence capabilities that are suitable for use with value chain applications (e.g., the set of demand management applications 824 and supply chain applications 812) that contribute to goods / services in the category of goods 3010. The user interface 3020 may facilitate providing coordinated intelligence that comprises artificial intelligence capabilities that provide coordinated intelligence for a specific operator and / or enterprise that participates in the supply chain for the category of goods.

[0755] In embodiments, the user interface 3020 may be configured to facilitate the user selecting and / or configuring multiple artificial intelligence systems 1160 for use with the value chain. The user interface may present the set of demand management applications 824 and supply chain applications 812 as connected entities that receive, process, and produce outputs each of which may be shared among the applications. Types of artificial intelligence systems 1160 may be indicated in the user interface 3020 responsive to sets of connected applications or their data elements being indicated in the user interface, such as by the user placing a pointer proximal to a connected set of applications and the like. In embodiments, the user interface 3020 may facilitate access to the set of adaptive intelligence systems provides a set of capabilities that facilitate development and deployment of intelligence for at least one function selected from a list of functions consisting of supply chain application automation, demand management application automation, machine learning, artificial intelligence, intelligent transactions, intelligent operations, remote control, analytics, monitoring, reporting, state management, event management, and process management.

[0756] The adaptive intelligence systems 614 may be configured with data processing, artificial intelligence and computational systems 634 that may operate cooperatively to provide coordinated intelligence, such as when an artificial intelligence system 1160 operates on or responds to data collected by or produced by other systems of the adaptive intelligence systems 614, such as a data processing system and the like. In embodiments, providing coordinated intelligence may include operating a portion of a set of artificial intelligence systems 1160 that employs one or more types of neural network that is described herein and in the documents incorporated herein by reference and that processes any of the demand management application outputs and supply chain application outputs to provide the coordinated intelligence.

[0757] In embodiments, providing coordinated intelligence for the set of demand management applications 824 may include configuring at least one of the adaptive intelligence systems 614 (e.g., through the user interface 3020 and the like) for at least one or more demand managementapplications selected from a list of demand management applications including a demand planning application, a demand prediction application, a sales application, a future demand aggregation application, a marketing application, an advertising application, an e-commerce application, a marketing analytics application, a customer relationship management application, a search engine optimization application, a sales management application, an advertising network application, a behavioral tracking application, a marketing analytics application, a location-based product or service-targeting application, a collaborative filtering application, a recommendation engine for a product or service, and the like.

[0758] Similarly, providing coordinated intelligence for the set of supply chain applications 812 may include configuring at least one of the adaptive intelligence systems 614 for at least one or more supply chain applications selected from a list of supply chain applications including a goods timing management application, a goods quantity management application, a logistics management application, a shipping application, a delivery application, an order for goods management application, an order for components management application, and the like.

[0759] In embodiments, the management platform 102 may, such as through the user interface 3020 facilitate access to the set of adaptive intelligence systems 614 that provide coordinated intelligence for a set of demand management applications 824 and supply chain applications 812 through the application of artificial intelligence. In such embodiments, the user may seek to align supply with demand while ensuring profitability and the like of a value chain for a category of goods 3010. By providing access to artificial intelligence capabilities 1160, the management platform allows the user to focus on the applications of demand and supply while gaining advantages of techniques such as expert systems, artificial intelligence systems, neural networks, supervised learning systems, machine learning systems, deep learning systems, and the like.

[0760] In embodiments, the management platform 102 may, through the user interface 3020 and the like provide a set of adaptive intelligence systems 614 that provide coordinated artificial intelligence 1160 for the sets of demand management applications 824 and supply chain applications 812 for the category of goods 3020 by, for example, determining (automatically) relationships among demand management and supply chain applications based on inputs used by the applications, results produced by the applications, and value chain outcomes. The artificial intelligence 1160 may be coordinated by, for example, the set of data processing, artificial intelligence and computational systems 634 available through the adaptive intelligence systems 614.

[0761] In embodiments, the management platform 102 may be configured with a set of artificial intelligence systems 1160 as part of a set of adaptive intelligence systems 614 that provide the coordinated intelligence for the sets of demand management applications 824 and supply chain applications 812 for a category of goods 3010. The set of artificial intelligence systems 1160 may provide the coordinated intelligence so that at least one supply chain application of the set of supply chain applications 812 produces results that address at least one aspect of supply for at least one of the goods in the category of goods as determined by at leastone demand management application of the set of demand management applications 824. In examples, a behavioral tracking demand management application may generate results for behavior of uses of a good in the category of goods 3010. The artificial intelligence systems 1160 may process the behavior data and conclude that there is a perceived need for greater consumer access to a second product in the category of goods 3010. This coordinated intelligence may be, optionally automatically, applied to the set of supply chain applications 812 so that, for example, production resources or other resources in the value chain for the category of goods are allocated to the second product. In examples, a distributor who handles stocking retailer shelves may receive a new stocking plan that allocates more retail shelf space for the second product, such as by taking away space from a lower margin product and the like.

[0762] In embodiments, the set of artificial intelligence systems 1160 and the like may provide coordinated intelligence for the sets of supply chain and demand management applications by, for example, determining an optionally temporal prioritization of demand management application outputs that impact control of supply chain applications so that an optionally temporal demand for at least one of the goods in the category of goods 3010 can be met. Seasonal adjustments in prioritization of demand application results are one example of a temporal change. Adjustments in prioritization may also be localized, such as when a large college football team is playing at their home stadium and local supply of tailgating supplies may temporally be adjusted even though demand management application results suggest that small propane stoves are not currently in demand in a wider region.

[0763] A set of adaptive intelligence systems 614 that provide coordinated intelligence, such as by providing artificial intelligence capabilities 1160 and the like may also facilitate development and deployment of intelligence for at least one function selected from a list of functions consisting of supply chain application automation, demand management application automation, machine learning, artificial intelligence, intelligent transactions, intelligent operations, remote control, analytics, monitoring, reporting, state management, event management, and process management. The set of adaptive intelligence systems 614 may be configured as a layer in the platform and an artificial intelligence system therein may operate on or be responsive to data collected by and / or produced by other systems (e.g., data processing systems, expert systems, machine learning systems and the like) of the adaptive intelligence systems layer.

[0764] In addition to providing coordinated intelligence configured for specific categories of goods, the coordinated intelligence may be provided for a specific value chain entity 652, such as a supply chain operator, business, enterprise, and the like that participates in the supply chain for the category of goods.

[0765] Providing coordinated intelligence may include employing a neural network to process at least one of the inputs and outputs of the sets of demand management and supply chain applications. Neural networks may be used with demand applications, such as a demand planning application, a demand prediction application, a sales application, a future demand aggregation application, a marketing application, an advertising application, an e-commerceapplication, a marketing analytics application, a customer relationship management application, a search engine optimization application, a sales management application, an advertising network application, a behavioral tracking application, a marketing analytics application, a location-based product or service-targeting application, a collaborative filtering application, a recommendation engine for a product or service, and the like. Neural networks may also be used with supply chain applications such as a goods timing management application, a goods quantity management application, a logistics management application, a shipping application, a delivery application, an order for goods management application, an order for components management application, and the like. Neural networks may provide coordinated intelligence by processing data that is available in any of a plurality of value chain data sources for the category of goods including without limitation processes, bill of materials, weather, traffic, design specification, customer complaint logs, customer reviews, Enterprise Resource Planning (ERP) System, Customer Relationship Management (CRM) System, Customer Experience Management (CEM) System, Service Lifecycle Management (SLM) System, Product Lifecycle Management (PLM) System, and the like. Neural networks configured for providing coordinated intelligence may share adaptation capabilities with other adaptive intelligence systems 614, such as when these systems are configured in a topology that facilitates such shared adaptation. In embodiments, neural networks may facilitate provisioning available value chain / supply chain network resources for both the set of demand management applications and for the set of supply chain applications. In embodiments, neural networks may provide coordinated intelligence to improve at least one of the list of outputs consisting of a process output, an application output, a process outcome, an application outcome, and the like.

[0766] Referring to FIG. 15, a management platform of an information technology system, such as a management platform for a value chain of goods and / or services is depicted as a block diagram of functional elements and representative interconnections. The management platform includes a user interface 3020 that provides, among other things, a hybrid set of adaptive intelligence systems 614. The hybrid set of adaptive intelligence systems 614 provide coordinated intelligence through the application of artificial intelligence, such as through application of a hybrid artificial intelligence system 3060, and optionally through one or more expert systems, machine learning systems, and the like for use with a set of demand management applications 824 and for a set of supply chain applications 812 for a category of goods 3010, which may be produced and sold through the value chain. The hybrid adaptive intelligence systems 614 may deliver two types of artificial intelligence systems, type A 3052 and type B 3054 through a set of data processing, artificial intelligence and computational systems 634. In embodiments, the hybrid adaptive intelligence systems 614 are selectable and / or configurable through the user interface 3020 so that one or more of the hybrid adaptive intelligence systems 614 can operate on or in cooperation with the sets of supply chain applications (e.g., demand management applications 824 and supply chain applications 812). The hybrid adaptive intelligence systems 614 may include a hybrid artificial intelligence system 3060 that may include at least two types of artificial intelligence capabilities including any of thevarious expert systems, artificial intelligence systems, neural networks, supervised learning systems, machine learning systems, deep learning systems, and other systems described throughout this disclosure and in the documents incorporated by reference. The hybrid adaptive intelligence systems 614 may facilitate applying a first type of artificial intelligence system 1160 to the set of demand management applications 824 and a second type of artificial intelligence system 1160 to the set of supply chain applications 812, wherein each of the first type and second type of artificial intelligence system 1160 can operate independently, cooperatively, and optionally coordinate operation to provide coordinated intelligence for operation of the value chain that produces at least one of the goods in the category of goods 3010.

[0767] In embodiments, the user interface 3020 may include interfaces for configuring a hybrid artificial intelligence system 3060 to take inputs from selected data sources of the value chain (such as data sources used by the set of demand management applications 824 and / or the set of supply chain applications 812) and supply them, such as to at least one of the two types of artificial intelligence systems in the hybrid artificial intelligence system 3060, types of which are described throughout this disclosure and in the documents incorporated herein by reference to enhance, control, improve, optimize, configure, adapt or have another impact on a value chain for the category of goods 3010. In embodiments, the selected data sources of the value chain may be applied either as inputs for classification or prediction, or as outcomes relating to the value chain, the category of goods 3010 and the like.

[0768] In embodiments, the hybrid adaptive intelligence systems 614 provides a plurality of distinct artificial intelligence systems 1160, a hybrid artificial intelligence system 3060, and combinations thereof. In embodiments, any of the plurality of distinct artificial intelligence systems 1160 and the hybrid artificial intelligence system 3060 may be configured as a plurality of neural network-based systems, such as a classification-adapted neural network, a prediction- adapted neural network and the like. As an example of hybrid adaptive intelligence systems 614, a machine learning-based artificial intelligence system may be provided for the set of demand management applications 824 and a neural network-based artificial intelligence system may be provided for the set of supply chain applications 812. As an example of a hybrid artificial intelligence system 3060, the hybrid adaptive intelligence systems 614 may provide the hybrid artificial intelligence system 3060 that may include a first type of artificial intelligence that is applied to the demand management applications 824 and which is distinct from a second type of artificial intelligence that is applied to the supply chain applications 812. A hybrid artificial intelligence system 3060 may include any combination of types of artificial intelligence systems including a plurality of a first type of artificial intelligence (e.g., neural networks) and at least one second type of artificial intelligence (e.g., an expert system) and the like. In embodiments, a hybrid artificial intelligence system may comprise a hybrid neural network that applies a first type of neural network with respect to the demand management applications 824 and a second type of neural network with respect to the supply chain applications 812. Yet further, a hybrid artificial intelligence system 3060 may provide two types of artificial intelligence to different applications, such as different demand management applications 824 (e.g., a sales managementapplication and a demand prediction application) or different supply chain applications 812 (e.g., a logistics control application and a production quality control application).

[0769] In embodiments, hybrid adaptive intelligence systems 614 may be applied as distinct artificial intelligence capabilities to distinct demand management applications 824. As examples, coordinated intelligence through a hybrid artificial intelligence capabilities may be provided to a demand planning application by a feed-forward neural network, to a demand prediction application by a machine learning system, to a sales application by a self-organizing neural network, to a future demand aggregation application by a radial basis function neural network, to a marketing application by a convolutional neural network, to an advertising application by a recurrent neural network, to an e-commerce application by a hierarchical neural network, to a marketing analytics application by a stochastic neural network, to a customer relationship management application by an associative neural network and the like.

[0770] Referring to FIG. 16, a management platform of an information technology system, such as a management platform for a value chain of goods and / or services is depicted as a block diagram of functional elements and representative interconnections for providing a set of predictions 3070. The management platform includes a user interface 3020 that provides, among other things, a set of adaptive intelligence systems 614. The adaptive intelligence systems 614 provide a set of predictions 3070 through the application of artificial intelligence, such as through application of an artificial intelligence system 1160, and optionally through one or more expert systems, machine learning systems, and the like for use with a coordinated set of demand management applications 824 and supply chain applications 812 for a category of goods 3010, which may be produced and sold through the value chain. The adaptive intelligence systems 614 may deliver the set of prediction 3070 through a set of data processing, artificial intelligence and computational systems 634. In embodiments, the adaptive intelligence systems 614 are selectable and / or configurable through the user interface 3020 so that one or more of the adaptive intelligence systems 614 can operate on or in cooperation with the coordinated sets of value chain applications. The adaptive intelligence systems 614 may include an artificial intelligence system that provides artificial intelligence capabilities known to be associated with artificial intelligence including any of the various expert systems, artificial intelligence systems, neural networks, supervised learning systems, machine learning systems, deep learning systems, and other systems described throughout this disclosure and in the documents incorporated by reference. The adaptive intelligence systems 614 may facilitate applying adapted intelligence capabilities to the coordinated set of demand management applications 824 and supply chain applications 812 such as by producing a set of predictions 3070 that may facilitate coordinating the two sets of value chain applications, or at least facilitate coordinating at least one demand management application and at least one supply chain application from their respective sets.

[0771] In embodiments, the set of predictions 3070 includes a least one prediction of an impact on a supply chain application based on a current state of a coordinated demand management application, such as a prediction that a demand for a good will decrease earlier than previously anticipated. The converse may also be true in that the set of predictions 3070 includes at leastone prediction of an impact on a demand management application based on a current state of a coordinated supply chain application, such as a prediction that a lack of supply of a good will likely impact a measure of demand of related goods. In embodiments, the set of predictions 3070 is a set of predictions of adjustments in supply required to meet demand. Other predictions include at least one prediction of change in demand that impacts supply. Yet other predictions in the set of predictions predict a change in supply that impacts at least one of the set of demand management applications, such as a promotion application for at least one good in the category of goods. A prediction in the set of predictions may be as simple as setting a likelihood that a supply of a good in the category of goods will not meet demand set by a demand setting application.

[0772] In embodiments, the adaptive intelligence systems 614 may provide a set of artificial intelligence capabilities to facilitate providing the set of predictions for the coordinated set of demand management applications and supply chain applications. In one non-limiting example, the set of artificial intelligence capabilities may include a probabilistic neural network that may be used to predict a fault condition or a problem state of a demand management application such as a lack of sufficient validated feedback. The probabilistic neural network may be used to predict a problem state with a machine performing a value chain operation (e.g., a production machine, an automated handling machine, a packaging machine, a shipping machine and the like) based on a collection of machine operating information and preventive maintenance information for the machine.

[0773] In embodiments, the set of predictions 3070 may be provided by the management platform 102 directly through a set of adaptive artificial intelligence systems.

[0774] In embodiments, the set of predictions 3070 may be provided for the coordinated set of demand management applications and supply chain applications for a category of goods by applying artificial intelligence capabilities for coordinating the set of demand management applications and supply chain applications.

[0775] In embodiments, the set of predictions 3070 may be predictions of outcomes for operating a value chain with the coordinated set demand management applications and supply chain applications for the category of goods, so that a user may conduct test cases of coordinated sets of demand management applications and supply chain applications to determine which sets may produce desirable outcomes (viable candidates for a coordinated set of applications) and which may produce undesirable outcomes.

[0776] Referring to FIG. 17, a management platform of an information technology system, such as a management platform for a value chain of goods and / or services is depicted as a block diagram of functional elements and representative interconnections for providing a set of classifications 3080. The management pla...

Claims

CLAIMS1. A robotic system comprising: a robotic control circuit configured to control one or more robotic functions of a robot; a plurality of sensors configured to collect data; a governance analysis circuit configured to analyze the data and select one or more governance frameworks based on the analyzed data; and a governance model circuit configured to generate a model that applies the one or more governance frameworks to determine one or more governance actions, wherein the robotic control circuit is configured to control the one or more robotic functions in accordance with the one or more governance actions; wherein the robotic control circuit, the governance analysis circuit, and the governance model circuit are integrated on a single substrate.

2. The robotic system of claim 1 wherein the one or more governance frameworks comprise at least one of safety standards, security standards, quality standards, regulatory standards, or financial standards.

3. The robotic system of claim 1 wherein the analyzed data indicates a state of an environment containing the robotic system.

4. The robotic system of claim 1 wherein the one or more governance frameworks comprise a plurality of governance frameworks, wherein the governance model circuit is further configured to: prioritize the plurality of governance frameworks; and resolve a conflict between a first governance framework of the plurality of governance frameworks and a second governance framework of the plurality of governance frameworks based on respective priorities of the first governance framework and the second governance framework.

5. The robotic system of claim 1 wherein the governance model circuit is further configured to apply the generated model to a second set of data captured after the generation of the model to determine the one or more governance actions.

6. The robotic system of claim 1 wherein the governance model circuit is configured to continually adjust the model based on real-time data captured from the plurality of sensors.

7. The robotic system of claim 1 wherein the robotic control circuit is further configured to control one or more functions of a second robotic system in accordance with the one or more governance actions.

8. The robotic system of claim 7 wherein the robotic system is further configured to transmit an instruction to the second robotic system to control the one or more functions of the second robotic system.

9. The robotic system of claim 1 wherein the one or more governance actions comprise changing a state of the robotic system.

10. The robotic system of claim 1 wherein the one or more governance actions comprise changing a task assigned to the robotic system.

11. The robotic system of claim 1 wherein the one or more governance actions comprise transmitting a warning or alarm.

12. The robotic system of claim 1 wherein the one or more governance actions comprise transforming data to comply with the one or more governance frameworks.

13. The robotic system of claim 1 wherein the governance model circuit is configured to simulate the one or more governance actions within a digital twin environment.

14. The robotic system of claim 1 wherein one or more of the robotic control circuit, the governance analysis circuit, and / or the governance model circuit are implemented using specialized Al chips.

15. The robotic system of claim 1 wherein one or more of the robotic control circuit, the governance analysis circuit, or the governance model circuit are implemented using a combination of CPUs and GPUs.

16. The robotic system of claim 1 wherein one or more of the robotic control circuit, the governance analysis circuit, or the governance model circuit are configured to use dynamic voltage and frequency scaling.

17. The robotic system of claim 1 wherein the single substrate includes a 2.5D or 3D stack of chips.

18. The robotic system of claim 1 wherein one or more of the robotic control circuit, the governance analysis circuit, or the governance model circuit are connected using a high-speed bridge.

19. The robotic system of claim 1 wherein one or more of the robotic control circuit, the governance analysis circuit, or the governance model circuit are connected to high bandwidth memory.

20. The robotic system of claim 1 wherein one or more of the robotic control circuit, the governance analysis circuit, and / or the governance model circuit are modular elements connected using die-to-die connectivity.

21. A robotic system comprising: a robotic control circuit configured to control one or more robotic functions of a robot; a plurality of sensors configured to collect data; a predictive modeling circuit configured to use one or more artificial intelligence models to generate a prediction based on the data; and a predictive model optimization circuit configured to re-train a predictive model of the one or more artificial intelligence models based on one or more conditions detected after generating the prediction, wherein the robotic control circuit, the predictive modeling circuit, and the predictive model optimization circuit are integrated on a single substrate.

22. The robotic system of claim 21 wherein the predictive model optimization circuit is further configured to train the predictive model based on training data generated by the robotic system.

23. The robotic system of claim 22 wherein the training data generated by the robotic system comprises classification data generated based on the data captured by the plurality of sensors.

24. The robotic system of claim 22 wherein the predictive model optimization circuit is further configured to train the predictive model based on training data generated by an environment digital twin.

25. The robotic system of claim 21 wherein the predictive model optimization circuit is configured to re-train the predictive model based on an accuracy of the prediction generated by the predictive modeling circuit.

26. The robotic system of claim 21 further comprising a recommendation circuit configured to provide a recommended action for the robotic system based on the prediction.

27. The robotic system of claim 26 wherein the robotic control system is configured to control the one or more robotic functions of the robot based on the recommended action.

28. The robotic system of claim 26 wherein the recommended action comprises an action and an entity on which the action will be taken.

29. The robotic system of claim 28 wherein the recommended action further comprises a modifier for the action.

30. The robotic system of claim 26 wherein the robotic system is further configured to simulate the recommended action using an environment digital twin.

31. The robotic system of claim 26 wherein the recommendation circuit is further configured to provide a second recommended action for controlling one or more robotic functions of a second robotic system.

32. The robotic system of claim 31 wherein the robotic system is further configured to transmit the second recommended action to the second robotic system.

33. The robotic system of claim 26 wherein the robotic system is further configured to generate a report indicating an outcome of the recommended action.

34. The robotic system of claim 21 wherein one or more of the robotic control circuit, the predictive modeling circuit, or the predictive model optimization circuit are implemented using specialized Al chips.

35. The robotic system of claim 21 wherein one or more of the robotic control circuit, the predictive modeling circuit, or the predictive model optimization circuit are implemented using a combination of CPUs and GPUs.

36. The robotic system of claim 21 wherein the one or more of the robotic control circuit, the predictive modeling circuit, or the predictive model optimization circuit are configured to use dynamic voltage and frequency scaling.

37. The robotic system of claim 21 wherein the single substrate includes a 2.5d or 3d stack of chips.

38. The robotic system of claim 21 wherein one or more of the robotic control circuit, the predictive modeling circuit, or the predictive model optimization circuit are connected using a high-speed bridge.

39. The robotic system of claim 21 wherein one or more of the robotic control circuit, the predictive modeling circuit, or the predictive model optimization circuit are connected to high bandwidth memory.

40. The robotic system of claim 21 wherein one or more of the robotic control circuit, the predictive modeling circuit, or the predictive model optimization circuit are modular elements connected using die-to-die connectivity.

41. A robotic system comprising: a robotic control circuit configured to control one or more robotic functions of a robot; a plurality of sensors configured to collect data; a network interface circuit configured to communicate with other robotic systems via a network; a network analysis circuit configured to use one or more artificial intelligence models to analyze the data and the communication with other robotic systems; and a network optimization circuit configured to optimize the communication with other robotic systems based on the analysis by the network analysis circuit, wherein the robotic control circuit, the network interface circuit, the network analysis circuit, and the network optimization circuit are integrated on a single substrate.

42. The robotic system of claim 41 wherein the data comprises at least one of: a physical signal measurement, network traffic, network device information, or network configuration data.

43. The robotic system of claim 41 wherein the network analysis circuit is configured to predict a future network condition.

44. The robotic system of claim 41 wherein the network optimization circuit is configured to optimize one or more of traffic flows between robotic systems on the network, data prioritization on the network, or protocols used by the robotic systems on the network.

45. The robotic system of claim 41 wherein the network analysis circuit is further configured to generate or update a network digital twin based on the analysis performed by the network analysis circuit.

46. The robotic system of claim 45 wherein the network optimization circuit is configured to simulate the optimization using the network digital twin.

47. The robotic system of claim 41 wherein the optimization performed by the network optimization circuit comprises optimizing a schedule of the network, a quality of data transmitted between robotic systems via the network, or a security of data transmitted between robotic systems via the network.

48. The robotic system of claim 41 wherein the optimization performed by the network optimization circuit comprises instructing a robotic system to power up or down, switch networks, adjust a transmission schedule, adjust a communication protocol, re-route traffic, or perform compression on data.

49. The robotic system of claim 41 wherein the optimization performed by the network optimization circuit comprises compressing, decompressing, up-sampling, down-sampling,reformatting, delaying, buffering, or rescheduling traffic transmitted to or from robotic systems via the network interface circuit.

50. The robotic system of claim 41 wherein the optimization performed by the network optimization circuit comprises modifying an instruction being routed to a robotic system via the network.

51. The robotic system of claim 41 wherein the optimization performed by the network optimization circuit comprises changing a topology of the network.

52. The robotic system of claim 41 wherein the optimization performed by the network optimization circuit comprises changing a header of a data packet being routed to a robotic system via the network.

53. The robotic system of claim 41 further comprising a governance circuit configured to monitor and apply governance actions to traffic transmitted between robotic systems via the network.

54. The robotic system of claim 41 wherein one or more of the robotic control circuit, the network interface circuit, the network analysis circuit, or the network optimization circuit are implemented using specialized Al chips.

55. The robotic system of claim 41 wherein one or more of the robotic control circuit, the network interface circuit, the network analysis circuit, or the network optimization circuit are implemented using a combination of CPUs and GPUs.

56. The robotic system of claim 41 wherein one or more of the robotic control circuit, the network interface circuit, the network analysis circuit, or the network optimization circuit are configured to use dynamic voltage and frequency scaling.

57. The robotic system of claim 41 wherein the single substrate includes a 2.5d or 3d stack of chips.

58. The robotic system of claim 41 wherein one or more of the robotic control circuit, the network interface circuit, the network analysis circuit, or the network optimization circuit are connected using a high-speed bridge.

59. The robotic system of claim 41 wherein one or more of the robotic control circuit, the network interface circuit, the network analysis circuit, or the network optimization circuit are connected to high bandwidth memory.

60. The robotic system of claim 41 wherein one or more of the robotic control circuit, the network interface circuit, the network analysis circuit, or the network optimization circuit are modular elements connected using die-to-die connectivity.

61. An integrated chipset, comprising: a robotic control circuit configured to control a set of functions for a set of robots; a data collection circuit configured to handle data from a set of loT devices; a neural network classifier configured to process the data from the loT devices to output a classification of a state of an environment wherein the robots operate; and a neural network control circuit configured to operate on the output of the neural network classifier to output an operational control parameter to the robotic control circuit,wherein the robotic control circuit, the data collection circuit, the neural network classifier and the neural network control circuit share a set of I / O capabilities of the integrated chipset.

62. The integrated chipset of claim 61 wherein the shared set of I / O capabilities comprises shared I / O ports.

63. The integrated chipset of claim 61 wherein the shared set of I / O capabilities comprises shared data.

64. The integrated chipset of claim 61 wherein the shared set of I / O capabilities comprises shared sensors.

65. The integrated chipset of claim 61 wherein the shared set of I / O capabilities comprises shared actuators.

66. The integrated chipset of claim 61 wherein the shared set of I / O capabilities comprises the set of functions for the set of robots.

67. The integrated chipset of claim 61 wherein the neural network classifier comprises an application-specific integrated circuit (ASIC).

68. The integrated chipset of claim 61 wherein the neural network classifier comprises a graphics processing unit (GPU) or tensor processing unit (TPU).

69. The integrated chipset of claim 61 wherein the neural network classifier comprises an FPGA.

70. The integrated chipset of claim 61 wherein the set of robots comprises a plurality of robots, wherein the integrated chipset is on board a robot that manages the plurality of robots.

71. The integrated chipset of claim 61 wherein the set of robots comprises a plurality of robots, wherein the integrated chipset is on board a platform that manages the plurality of robots.

72. The integrated chipset of claim 61 wherein the neural network classifier is configured to recognize objects within the environment.

73. The integrated chipset of claim 61 wherein the neural network classifier is configured to perform scene understanding.

74. The integrated chipset of claim 61 wherein the neural network classifier is configured to determine an activity of a human within the environment.

75. The integrated chipset of claim 61 wherein the neural network classifier is configured to recognize sounds in the environment.

76. The integrated chipset of claim 61 wherein the neural network classifier is configured to determine a pose of at least one robot of the set of robots.

77. The integrated chipset of claim 61 wherein the neural network classifier is configured to determine a map of the environment.

78. The integrated chipset of claim 61 wherein the neural network control circuit is configured to control navigation within the environment.

79. The integrated chipset of claim 61 wherein the neural network control circuit is configured to control interactions with objects in the environment.

80. The integrated chipset of claim 61 wherein the neural network control circuit is configured to control interactions with humans in the environment.

81. An integrated chipset, comprising: a robotic control circuit configured to control a set of functions for a set of robots; a data collection circuit configured to handle data from a set of loT devices; a neural network classifier configured to process the data from the loT devices to ouptut a classification of a state of an environment wherein the robots operate; and a neural network control circuit configured to operate on the output of the neural network classifier to output an operational control parameter to the robotic control circuit, wherein the robotic control circuit, the data collection circuit, the neural network classifier and the neural network control circuit are integrated on a common substrate82. The integrated chipset of claim 81 wherein the robotic control circuit, the data collection circuit, the neural network classifier, and the neural network control circuit are manufactured on a single silicon wafer, wherein the common substrate is the single silicon wafer.

83. The integrated chipset of claim 81 the common substrate is a single chip.

84. The integrated chipset of claim 81 wherein the robotic control circuit, the data collection circuit, the neural network classifier, and the neural network control circuit are separately manufactured chips that are bonded to the common substrate.

85. The integrated chipset of claim 81 wherein the common substrate is a package, wherein the robotic control circuit, the data collection circuit, the neural network classifier, and the neural network control circuit are enclosed in the package.

86. The integrated chipset of claim 85 wherein the package encloses a plurality of packages, wherein the plurality of packages are connected via a common interface.

87. The integrated chipset of claim 81 wherein the set of robots comprises a plurality of robots, wherein the integrated chipset is on board a robot that manages the plurality of robots.

88. The integrated chipset of claim 81 wherein the set of robots comprises a plurality of robots, wherein the integrated chipset is on board a platform that manages the plurality of robots.

89. The integrated chipset of claim 81 wherein the neural network classifier is configured to recognize objects within the environment.

90. The integrated chipset of claim 81 wherein the neural network classifier is configured to perform scene understanding.

91. The integrated chipset of claim 81 wherein the neural network classifier is configured to determine an activity of a human within the environment.

92. The integrated chipset of claim 81 wherein the neural network classifier is configured to recognize sounds in the environment.

93. The integrated chipset of claim 81 wherein the neural network classifier is configured to determine a pose of at least one robot of the set of robots.

94. The integrated chipset of claim 81 wherein the neural network classifier is configured to determine a map of the environment.

95. The integrated chipset of claim 81 wherein the neural network control circuit is configured to control navigation within the environment.

96. The integrated chipset of claim 81 wherein the neural network control circuit is configured to control interactions with objects in the environment.

97. The integrated chipset of claim 81 wherein the neural network control circuit is configured to control interactions with humans in the environment.

98. A method performed by an integrated chipset, the method comprising: controlling, by a robotic control circuit of the integrated chipset, a set of functions for a set of robots; collecting, by a data collection circuit of the integrated chipset, data from a set of loT devices; processing, by a neural network classifier of the integrated chipset, the data from the loT devices to classify a state of an environment wherein the robots operate; and processing, by a neural network control circuit of the integrated chipset, an output of the neural network classifier to output an operational control parameter to the robotic control circuit, wherein the robotic control circuit, the data collection circuit, the neural network classifier and the neural network control circuit are integrated on a common substrate.

99. The method of claim 98 wherein the set of robots comprises a plurality of robots, wherein the integrated chipset is on board a robot that manages the plurality of robots.

100. The method of claim 98 wherein the set of robots comprises a plurality of robots, wherein the integrated chipset is on board a platform that manages the plurality of robots.

101. An integrated chipset, comprising: a robotic control circuit configured to control a set of functions for a set of robots; a data collection circuit configured to handle data from a set of loT devices; a neural network classifier configured to process the data from the loT devices to output a classification of a state of an environment wherein the robots operate; and a neural network control circuit configured to operate on the output of the neural network classifier circuit to output an operational control parameter to the robotic control circuit, wherein the robotic control circuit, the data collection circuit, the neural network classifier, and the neural network control circuit are configured as layers in a 3D chipset architecture.

102. The integrated chipset of claim 101 wherein the robotic control circuit, the data collection circuit, the neural network classifier, and the neural network control circuit are connected vertically using through-silicon vias.

103. The integrated chipset of claim 101 wherein the integrated chipset is within a package enclosing a plurality of vertically stacked packages, wherein the plurality of vertically stacked packages is connected via a common interface.

104. The integrated chipset of claim 101 wherein the set of robots comprises a plurality of robots, wherein the integrated chipset is on board a robot that manages the plurality of robots.

105. The integrated chipset of claim 101 wherein the set of robots comprises a plurality of robots, wherein the integrated chipset is on board a platform that manages the plurality of robots.

106. The integrated chipset of claim 101 wherein the neural network classifier is configured to recognize objects within the environment.

107. The integrated chipset of claim 101 wherein the neural network classifier is configured to perform scene understanding.

108. The integrated chipset of claim 101 wherein the neural network classifier is configured to determine an activity of a human within the environment.

109. The integrated chipset of claim 101 wherein the neural network classifier is configured to recognize sounds in the environment.

110. The integrated chipset of claim 101 wherein the neural network classifier is configured to determine a pose of at least one robot of the set of robots.

111. The integrated chipset of claim 101 wherein the neural network classifier is configured to determine a map of the environment.

112. The integrated chipset of claim 101 wherein the neural network control circuit is configured to control navigation within the environment.

113. The integrated chipset of claim 101 wherein the neural network control circuit is configured to control interactions with objects in the environment.

114. The integrated chipset of claim 101 wherein the neural network control circuit is configured to control interactions with humans in the environment.

115. A method performed by an integrated chipset, the method comprising: controlling, by a robotic control circuit of the integrated chipset, a set of functions for a set of robots; collecting, by a data collection circuit of the integrated chipset, data from a set of loT devices; processing, by a neural network classifier of the integrated chipset, the data from the loT devices to output a classification of a state of an environment wherein the robots operate; and processing, by a neural network control circuit of the integrated chipset, the output of the neural network classifier to output an operational control parameter to the robotic control circuit, wherein the robotic control circuit, the data collection circuit, the neural network classifier, and the neural network control circuit are configured as layers in a 3D chipset architecture.

116. The method of claim 115 wherein the set of robots comprises a plurality of robots, wherein the integrated chipset is on board a robot that manages the plurality of robots.

117. The method of claim 115 wherein the set of robots comprises a plurality of robots, wherein the integrated chipset is on board a platform that manages the plurality of robots.

118. The method of claim 115 wherein classifying the state of the environment comprises recognizing objects within the environment.

119. The method of claim 115 wherein classifying the state of the environment comprises determining a pose of at least one robot of the set of robots.

120. The method of claim 115 wherein classifying the state of the environment comprises determining a map of the environment.

121. An integrated chipset, comprising: a robotic control circuit configured to control a set of functions for a set of robots; a data collection circuit configured to handle data from a set of loT devices; a neural network classifier configured to process the data from the loT devices to ouptut a classification of a state of an environment wherein the robots operate; and a neural network control circuit configured to operate on the output of the neural network classifier to output an operational control parameter to the robotic control circuit, wherein the robotic control circuit, the data collection circuit, the neural network classifier and the neural network control circuit share a set of I / O capabilities that are disposed on a perimeter of the chipset.

122. The integrated chipset of claim 121 wherein the shared set of I / O capabilities comprises shared I / O ports disposed on a perimeter of the chipset.

123. The integrated chipset of claim 122 wherein the shared I / O ports are used to send and receive data to and from shared sensors and actuators.

124. The integrated chipset of claim 121 wherein the shared set of I / O capabilities comprises the set of functions for the set of robots.

125. The integrated chipset of claim 121 wherein the set of robots comprises a plurality of robots, wherein the integrated chipset is on board a robot that manages the plurality of robots.

126. The integrated chipset of claim 121 wherein the set of robots comprises a plurality of robots, wherein the integrated chipset is on board a platform that manages the plurality of robots.

127. The integrated chipset of claim 121 wherein the neural network classifier is configured to recognize objects within the environment.

128. The integrated chipset of claim 121 wherein the neural network classifier is configured to perform scene understanding.

129. The integrated chipset of claim 121 wherein the neural network classifier is configured to determine an activity of a human within the environment.

130. The integrated chipset of claim 121 wherein the neural network classifier is configured to recognize sounds in the environment.

131. The integrated chipset of claim 121 wherein the neural network classifier is configured to determine a pose of at least one robot of the set of robots.

132. The integrated chipset of claim 121 wherein the neural network classifier is configured to determine a map of the environment.

133. The integrated chipset of claim 121 wherein the neural network control circuit is configured to control navigation within the environment.

134. The integrated chipset of claim 121 wherein the neural network control circuit is configured to control interactions with objects in the environment.

135. The integrated chipset of claim 121 wherein the neural network control circuit is configured to control interactions with humans in the environment.

136. A method performed by an integrated chipset, the method comprising: controlling, by a robotic control circuit of the integrated chipset, a set of functions for a set of robots; collecting, by a data collection circuit of the integrated chipset, data from a set of loT devices; processing, by a neural network classifier of the integrated chipset, the data from the loT devices to output a classification of a state of an environment wherein the robots operate; and processing, by a neural network control circuit of the integrated chipset, the output of the neural network classifier to output an operational control parameter to the robotic control circuit, wherein the robotic control circuit, the data collection circuit, the neural network classifier and the neural network control circuit share a set of I / O capabilities that are disposed on a perimeter of the chipset.

137. The method of claim 136 wherein the set of robots comprises a plurality of robots, wherein the integrated chipset is on board a robot that manages the plurality of robots.

138. The method of claim 136 wherein the set of robots comprises a plurality of robots, wherein the integrated chipset is on board a platform that manages the plurality of robots.

139. The method of claim 136 wherein classifying the state of the environment comprises recognizing objects within the environment.

140. The method of claim 136 wherein classifying the state of the environment comprises determining a pose of at least one robot of the set of robots.

141. An integrated chipset, comprising: a robotic control circuit configured to control a set of functions for a set of robots; a data collection circuit configured to handle data from a set of loT devices; a neural network classifier configured to process the data from the loT devices to output a classification of a state of an environment wherein the robots operate; and a neural network control circuit configured to operate on the output of the neural network classifier to output an operational control parameter to the robotic control circuit, wherein the robotic control circuit, the data collection circuit, the neural network classifier and the neural network control circuit share are integrated on the chipset having a set of processing cores concentrated in a middle portion of the chipset served by a set of I / O capabilities located on a perimeter of the chipset with an off-chip interconnection capability substantially at the center of the chipset.

142. The integrated chipset of claim 141 wherein the shared set of I / O capabilities comprises shared I / O ports disposed on the perimeter of the chipset.

143. The integrated chipset of claim 142 wherein the shared I / O ports are used to send and receive data to and from shared sensors and actuators.

144. The integrated chipset of claim 141 wherein the shared set of I / O capabilities comprises the set of functions for the set of robots.

145. The integrated chipset of claim 141 wherein the set of robots comprises a plurality of robots, wherein the integrated chipset is on board a robot that manages the plurality of robots.

146. The integrated chipset of claim 141 wherein the set of robots comprises a plurality of robots, wherein the integrated chipset is on board a platform that manages the plurality of robots.

147. The integrated chipset of claim 141 wherein the neural network classifier is configured to recognize objects within the environment.

148. The integrated chipset of claim 141 wherein the neural network classifier is configured to perform scene understanding.

149. The integrated chipset of claim 141 wherein the neural network classifier is configured to determine an activity of a human within the environment.

150. The integrated chipset of claim 141 wherein the neural network classifier is configured to recognize sounds in the environment.

151. The integrated chipset of claim 141 wherein the neural network classifier is configured to determine a pose of at least one robot of the set of robots.

152. The integrated chipset of claim 141 wherein the neural network classifier is configured to determine a map of the environment.

153. The integrated chipset of claim 141 wherein the neural network control circuit is configured to control navigation within the environment.

154. The integrated chipset of claim 141 wherein the neural network control circuit is configured to control interactions with objects in the environment.

155. The integrated chipset of claim 141 wherein the neural network control circuit is configured to control interactions with humans in the environment.

156. A method performed by an integrated chipset, the method comprising: controlling, by a robotic control circuit of the integrated chipset, a set of functions for a set of robots; collecting, by a data collection circuit of the integrated chipset, data from a set of loT devices; processing, by a neural network classifier of the integrated chipset, the data from the loT devices to output a classification of a state of an environment wherein the robots operate; and processing, by a neural network control circuit of the integrated chipset, the output of the neural network classifier circuit to output an operational control parameter to the robotic control circuit, wherein the robotic control circuit, the data collection circuit, the neural network classifier and the neural network control circuit share are integrated on the chipset having a set of processing cores concentrated in a middle portion of the chipset served by a set of I / O capabilities located on a perimeter of the chipset with an off-chip interconnection capability substantially at the center of the chipset.

157. The method of claim 156 wherein the set of robots comprises a plurality of robots, wherein the integrated chipset is on board a robot that manages the plurality of robots.

158. The method of claim 156 wherein the set of robots comprises a plurality of robots, wherein the integrated chipset is on board a platform that manages the plurality of robots.

159. The method of claim 156 wherein classifying the state of the environment comprises recognizing objects within the environment.

160. The method of claim 156 wherein classifying the state of the environment comprises determining a pose of at least one robot of the set of robots.

161. An integrated chipset, comprising: a robotic control circuit configured to control a set of functions for a set of robots; a data collection circuit configured to handle data from a set of loT devices; a neural network classifier configured to process the data from the loT devices to output a classification of a state of an environment wherein the robots operate; and a neural network control circuit configured to operate on the output of the neural network classifier to output an operational control parameter to the robotic control circuit, wherein the robotic control circuit, the data collection circuit, the neural network classifier, and the neural network control circuit are integrated on the chipset having a set of modular processing clusters connected by a set of embedded multi-chip interconnect bridges to a set of high bandwidth memory modules.

162. The integrated chipset of claim 161 wherein a first module processing cluster comprises the neural network classifier and the neural network control circuit.

163. The integrated chipset of claim 162 wherein a second module processing cluster comprises the robotic control circuit and the data collection circuit.

164. The integrated chipset of claim 161 wherein each of the high bandwidth memory modules is a HBM module, a HBM2 module, a HBM2E module, or a HBM3 module.

165. The integrated chipset of claim 161 wherein the embedded multi-chip interconnect bridge is one of a network on chip (NoC) bridge, an advanced extensible interface (AXI) bridge, a PCI express (PCIe) bridge, a high-speed inter-chip (HSIC) bridge, or a hypertransport bridge.

166. The integrated chipset of claim 161 wherein the set of robots comprises a plurality of robots, wherein the integrated chipset is on board a robot that manages the plurality of robots.

167. The integrated chipset of claim 161 wherein the set of robots comprises a plurality of robots, wherein the integrated chipset is on board a platform that manages the plurality of robots.

168. The integrated chipset of claim 161 wherein the neural network classifier is configured to recognize objects within the environment.

169. The integrated chipset of claim 161 wherein the neural network classifier is configured to perform scene understanding.

170. The integrated chipset of claim 161 wherein the neural network classifier is configured to determine an activity of a human within the environment.

171. The integrated chipset of claim 161 wherein the neural network classifier is configured to recognize sounds in the environment.

172. The integrated chipset of claim 161 wherein the neural network classifier is configured to determine a pose of at least one robot of the set of robots.

173. The integrated chipset of claim 161 wherein the neural network classifier is configured to determine a map of the environment.

174. The integrated chipset of claim 161 wherein the neural network control circuit is configured to control navigation within the environment.

175. The integrated chipset of claim 161 wherein the neural network control circuit is configured to control interactions with objects in the environment.

176. The integrated chipset of claim 161 wherein the neural network control circuit is configured to control interactions with humans in the environment.

177. A method performed by an integrated chipset, the method comprising: controlling, by a robotic control circuit of the integrated chipset, a set of functions for a set of robots; collecting, by a data collection circuit of the integrated chipset, data from a set of loT devices; processing, by a neural network classifier of the integrated chipset, the data from the loT devices to output a classification of a state of an environment wherein the robots operate; and processing, by a neural network control circuit of the integrated chipset, the output of the neural network classifier to output an operational control parameter to the robotic control circuit, wherein the robotic control circuit, the data collection circuit, the neural network classifier, and the neural network control circuit are integrated on the chipset having a set of modular processing clusters connected by a set of embedded multi-chip interconnect bridges to a set of high bandwidth memory modules.

178. The method of claim 177 wherein the set of robots comprises a plurality of robots, wherein the integrated chipset is on board a robot that manages the plurality of robots.

179. The method of claim 177 wherein the set of robots comprises a plurality of robots, wherein the integrated chipset is on board a platform that manages the plurality of robots.

180. The method of claim 177 wherein classifying the state of the environment comprises recognizing objects within the environment.

181. An integrated chipset, comprising: a robotic control circuit configured to control a set of functions for a set of robots; a data collection circuit configured to handle data from a set of loT devices; a neural network classifier configured to process the data from the loT devices to output a classification of a state of an environment wherein the robots operate; and a neural network control circuit configured to operate on the output of the neural network classifier to output an operational control parameter to the robotic control circuit, wherein the robotic control circuit, the data collection circuit, the neural network classifier and the neural network control circuit share are integrated on the chipset having a bidirectional torus network on chip architecture.

182. The integrated chipset of claim 181 wherein the set of robots comprises a plurality of robots, wherein the integrated chipset is on board a robot that manages the plurality of robots.

183. The integrated chipset of claim 181 wherein the set of robots comprises a plurality of robots, wherein the integrated chipset is on board a platform that manages the plurality of robots.

184. The integrated chipset of claim 181 wherein the neural network classifier is configured to recognize objects within the environment.

185. The integrated chipset of claim 181 wherein the neural network classifier is configured to perform scene understanding.

186. The integrated chipset of claim 181 wherein the neural network classifier is configured to determine an activity of a human within the environment.

187. The integrated chipset of claim 181 wherein the neural network classifier is configured to recognize sounds in the environment.

188. The integrated chipset of claim 181 wherein the neural network classifier is configured to determine a pose of at least one robot of the set of robots.

189. The integrated chipset of claim 181 wherein the neural network classifier is configured to determine a map of the environment.

190. The integrated chipset of claim 181 wherein the neural network control circuit is configured to control navigation within the environment.

191. The integrated chipset of claim 181 wherein the neural network control circuit is configured to control interactions with objects in the environment.

192. The integrated chipset of claim 181 wherein the neural network control circuit is configured to control interactions with humans in the environment.

193. A method performed by an integrated chipset, the method comprising: controlling, by a robotic control circuit of the integrated chipset, a set of functions for a set of robots; collecting, by a data collection circuit of the integrated chipset, data from a set of loT devices; processing, by a neural network classifier of the integrated chipset, the data from the loT devices to output a classification of a state of an environment wherein the robots operate; and processing, by a neural network control circuit of the integrated chipset, the output of the neural network classifier to output an operational control parameter to the robotic control circuit, wherein the robotic control circuit, the data collection circuit, the neural network classifier and the neural network control circuit share are integrated on the chipset having a bidirectional torus network on chip architecture.

194. The method of claim 193 wherein the set of robots comprises a plurality of robots, wherein the integrated chipset is on board a robot that manages the plurality of robots.

195. The method of claim 193 wherein the set of robots comprises a plurality of robots, wherein the integrated chipset is on board a platform that manages the plurality of robots.

196. The method of claim 193 wherein classifying the state of the environment comprises recognizing objects within the environment.

197. The method of claim 193 wherein classifying the state of the environment comprises determining a pose of at least one robot of the set of robots.

198. The method of claim 193 wherein classifying the state of the environment comprises determining a map of the environment.

199. The method of claim 193 wherein the operational control parameter is configured to control navigation within the environment.

200. The method of claim 193 wherein the operational control parameter is configured to control interactions with humans or objects in the environment.

201. An integrated chipset, comprising: a robotic control circuit configured to control a set of functions for a set of robots; a data collection circuit configured to handle data from a set of loT devices; a neural network classifier configured to process the data from the loT devices to output a classification of a state of an environment wherein the robots operate; and a neural network control circuit configured to operate on the output of the neural network classifier to output an operational control parameter to the robotic control circuit, wherein the robotic control circuit, the data collection circuit, the neural network classifier, and the neural network control circuit share are integrated on the chipset as a set of modular elements using die-to-die connectivity.

202. The integrated chipset of claim 201 wherein at least a subset of the modular elements are arranged in a 2.5D or 3D stacked configuration.

203. The integrated chipset of claim 202 wherein the die-to-die connectivity uses silicon interposers.

204. The integrated chipset of claim 201 wherein the die-to-die connectivity is one or more of Embedded Multi-Die Interconnect Bridge (EMIB), Advanced Interconnect Bus (AIB), Chip-to- Chip Direct Connect (C2C).

205. The integrated chipset of claim 201 wherein the chipset is integrated within a package using Wafer-Level Fan-Out (WLFO) and / or Fan-Out Wafer-Level Packaging (FOWLP).

206. The integrated chipset of claim 201 wherein the set of robots comprises a plurality of robots, wherein the integrated chipset is on board a robot that manages the plurality of robots.

207. The integrated chipset of claim 201 wherein the set of robots comprises a plurality of robots, wherein the integrated chipset is on board a platform that manages the plurality of robots.

208. The integrated chipset of claim 201 wherein the neural network classifier is configured to recognize objects within the environment.

209. The integrated chipset of claim 201 wherein the neural network classifier is configured to perform scene understanding.

210. The integrated chipset of claim 201 wherein the neural network classifier is configured to determine an activity of a human within the environment.

211. The integrated chipset of claim 201 wherein the neural network classifier is configured to recognize sounds in the environment.

212. The integrated chipset of claim 201 wherein the neural network classifier is configured to determine a pose of at least one robot of the set of robots.

213. The integrated chipset of claim 201 wherein the neural network classifier is configured to determine a map of the environment.

214. The integrated chipset of claim 201 wherein the neural network control circuit is configured to control navigation within the environment.

215. The integrated chipset of claim 201 wherein the neural network control circuit is configured to control interactions with objects in the environment.

216. The integrated chipset of claim 201 wherein the neural network control circuit is configured to control interactions with humans in the environment.

217. A method performed by an integrated chipset, the method comprising: controlling, by a robotic control circuit of the integrated chipset, a set of functions for a set of robots; collecting, by a data collection circuit of the integrated chipset, data from a set of loT devices; processing, by a neural network classifier of the integrated chipset, the data from the loT devices to output a classification of a state of an environment wherein the robots operate; and processing, by a neural network control circuit of the integrated chipset, the output of the neural network classifier to output an operational control parameter to the robotic control circuit, wherein the robotic control circuit, the data collection circuit, the neural network classifier, and the neural network control circuit share are integrated on the chipset as a set of modular elements using die-to-die connectivity.

218. The method of claim 217 wherein the set of robots comprises a plurality of robots, wherein the integrated chipset is on board a robot that manages the plurality of robots.

219. The method of claim 217 wherein the set of robots comprises a plurality of robots, wherein the integrated chipset is on board a platform that manages the plurality of robots.

220. The method of claim 217 wherein classifying the state of the environment comprises recognizing objects within the environment.

221. An integrated chipset, comprising: a robotic control circuit configured to control a set of functions for a set of robots; a data collection circuit configured to handle data from a set of loT devices; a neural network classifier configured to process the data from the loT devices to output a classification of a state of an environment wherein the robots operate; and a neural network control circuit configured to operate on the output of the neural network classifier to output an operational control parameter to the robotic control circuit, wherein the robotic control circuit, the data collection circuit, the neural network classifier and the neural network control circuit share are integrated on the chipset using dynamic voltage and frequency scaling.

222. The integrated chipset of claim 221 wherein the set of robots comprises a plurality of robots, wherein the integrated chipset is on board a robot that manages the plurality of robots.

223. The integrated chipset of claim 221 wherein the set of robots comprises a plurality of robots, wherein the integrated chipset is on board a platform that manages the plurality of robots.

224. The integrated chipset of claim 221 wherein the neural network classifier is configured to recognize objects within the environment.

225. The integrated chipset of claim 221 wherein the neural network classifier is configured to perform scene understanding.

226. The integrated chipset of claim 221 wherein the neural network classifier is configured to determine an activity of a human within the environment.

227. The integrated chipset of claim 221 wherein the neural network classifier is configured to recognize sounds in the environment.

228. The integrated chipset of claim 221 wherein the neural network classifier is configured to determine a pose of at least one robot of the set of robots.

229. The integrated chipset of claim 221 wherein the neural network classifier is configured to determine a map of the environment.

230. The integrated chipset of claim 221 wherein the neural network control circuit is configured to control navigation within the environment.

231. The integrated chipset of claim 221 wherein the neural network control circuit is configured to control interactions with objects in the environment.

232. The integrated chipset of claim 221 wherein the neural network control circuit is configured to control interactions with humans in the environment.

233. A method performed by an integrated chipset, the method comprising: controlling, by a robotic control circuit of the integrated chipset, a set of functions for a set of robots; collecting, by a data collection circuit of the integrated chipset, data from a set of loT devices; processing, by a neural network classifier of the integrated chipset, the data from the loT devices to output a classification of a state of an environment wherein the robots operate; and processing, by a neural network control circuit of the integrated chipset, the output of the neural network classifier to output an operational control parameter to the robotic control circuit, wherein the robotic control circuit, the data collection circuit, the neural network classifier and the neural network control circuit share are integrated on the chipset using dynamic voltage and frequency scaling.

234. The method of claim 233 wherein the set of robots comprises a plurality of robots, wherein the integrated chipset is on board a robot that manages the plurality of robots.

235. The method of claim 233 wherein the set of robots comprises a plurality of robots, wherein the integrated chipset is on board a platform that manages the plurality of robots.

236. The method of claim 233 wherein classifying the state of the environment comprises recognizing objects within the environment.

237. The method of claim 233 wherein classifying the state of the environment comprises determining a pose of at least one robot of the set of robots.

238. The method of claim 233 wherein classifying the state of the environment comprises determining a map of the environment.

239. The method of claim 233 wherein the operational control parameter is configured to control navigation within the environment.

240. The method of claim 233 wherein the operational control parameter is configured to control interactions with humans or objects in the environment.

241. An integrated chipset, comprising: a robotic control circuit configured to control a set of functions for a set of robots; a data collection circuit configured to handle data from a set of loT devices; a neural network classifier configured to process the data from the loT devices to output a classification of a state of an environment wherein the robots operate; and a neural network control circuit configured to operate on the output of the neural network classifier to output an operational control parameter to the robotic control circuit, wherein the robotic control circuit, the data collection circuit, the neural network classifier and the neural network control circuit share are integrated on an optical chipset where optical communication is partitioned by wavelength to allow selective prioritization by wavelength.

242. The integrated chipset of claim 241 wherein the set of robots comprises a plurality of robots, wherein the integrated chipset is on board a robot that manages the plurality of robots.

243. The integrated chipset of claim 241 wherein the set of robots comprises a plurality of robots, wherein the integrated chipset is on board a platform that manages the plurality of robots.

244. The integrated chipset of claim 241 wherein the neural network classifier is configured to recognize objects within the environment.

245. The integrated chipset of claim 241 wherein the neural network classifier is configured to perform scene understanding.

246. The integrated chipset of claim 241 wherein the neural network classifier is configured to determine an activity of a human within the environment.

247. The integrated chipset of claim 241 wherein the neural network classifier is configured to recognize sounds in the environment.

248. The integrated chipset of claim 241 wherein the neural network classifier is configured to determine a pose of at least one robot of the set of robots.

249. The integrated chipset of claim 241 wherein the neural network classifier is configured to determine a map of the environment.

250. The integrated chipset of claim 241 wherein the neural network control circuit is configured to control navigation within the environment.

251. The integrated chipset of claim 241 wherein the neural network control circuit is configured to control interactions with objects in the environment.

252. The integrated chipset of claim 241 wherein the neural network control circuit is configured to control interactions with humans in the environment.

253. A method performed by an integrated chipset, the method comprising: controlling, by a robotic control circuit of the integrated chipset, a set of functions for a set of robots; collecting, by a data collection circuit of the integrated chipset, data from a set of loT devices;processing, by a neural network classifier of the integrated chipset, the data from the loT devices to output a classification of a state of an environment wherein the robots operate; and processing, by a neural network control circuit of the integrated chipset, the output of the neural network classifier to output an operational control parameter to the robotic control circuit, wherein the robotic control circuit, the data collection circuit, the neural network classifier and the neural network control circuit share are integrated on an optical chipset where optical communication is partitioned by wavelength to allow selective prioritization by wavelength.

254. The method of claim 253 wherein the set of robots comprises a plurality of robots, wherein the integrated chipset is on board a robot that manages the plurality of robots.

255. The method of claim 253 wherein the set of robots comprises a plurality of robots, wherein the integrated chipset is on board a platform that manages the plurality of robots.

256. The method of claim 253 wherein classifying the state of the environment comprises recognizing objects within the environment.

257. The method of claim 253 wherein classifying the state of the environment comprises determining a pose of at least one robot of the set of robots.

258. The method of claim 253 wherein classifying the state of the environment comprises determining a map of the environment.

259. The method of claim 253 wherein the operational control parameter is configured to control navigation within the environment.

260. The method of claim 253 wherein the operational control parameter is configured to control interactions with humans or objects in the environment.

261. An integrated chipset, comprising: a robotic control circuit configured to control a set of functions for a set of robots; a data collection circuit configured to handle data from a set of loT devices; a neural network classifier configured to process the data from the loT devices to output a classification of a state of an environment wherein the robots operate; and a neural network control circuit configured to operate on the output of the neural network classifier to output an operational control parameter to the robotic control circuit, wherein the robotic control circuit, the data collection circuit, the neural network classifier and the neural network control circuit share are integrated on the chipset using integrated fan-out packaging.

262. The integrated chipset of claim 261 wherein the set of robots comprises a plurality of robots, wherein the integrated chipset is on board a robot that manages the plurality of robots.

263. The integrated chipset of claim 261 wherein the set of robots comprises a plurality of robots, wherein the integrated chipset is on board a platform that manages the plurality of robots.

264. The integrated chipset of claim 261 wherein the neural network classifier is configured to recognize objects within the environment.

265. The integrated chipset of claim 261 wherein the neural network classifier is configured to perform scene understanding.

266. The integrated chipset of claim 261 wherein the neural network classifier is configured to determine an activity of a human within the environment.

267. The integrated chipset of claim 261 wherein the neural network classifier is configured to recognize sounds in the environment.

268. The integrated chipset of claim 261 wherein the neural network classifier is configured to determine a pose of at least one robot of the set of robots.

269. The integrated chipset of claim 261 wherein the neural network classifier is configured to determine a map of the environment.

270. The integrated chipset of claim 261 wherein the neural network control circuit is configured to control navigation within the environment.

271. The integrated chipset of claim 261 wherein the neural network control circuit is configured to control interactions with objects in the environment.

272. The integrated chipset of claim 261 wherein the neural network control circuit is configured to control interactions with humans in the environment.

273. A method performed by an integrated chipset, the method comprising: controlling, by a robotic control circuit of the integrated chipset, a set of functions for a set of robots; collecting, by a data collection circuit of the integrated chipset, data from a set of loT devices; processing, by a neural network classifier of the integrated chipset, the data from the loT devices to output a classification of a state of an environment wherein the robots operate; and processing, by a neural network control circuit of the integrated chipset, the output of the neural network classifier circuit to output an operational control parameter to the robotic control circuit, wherein the robotic control circuit, the data collection circuit, the neural network classifier and the neural network control circuit share are integrated on the chipset using integrated fan-out packaging.

274. The method of claim 273 wherein the set of robots comprises a plurality of robots, wherein the integrated chipset is on board a robot that manages the plurality of robots.

275. The method of claim 273 wherein the set of robots comprises a plurality of robots, wherein the integrated chipset is on board a platform that manages the plurality of robots.

276. The method of claim 273 wherein classifying the state of the environment comprises recognizing objects within the environment.

277. The method of claim 273 wherein classifying the state of the environment comprises determining a pose of at least one robot of the set of robots.

278. The method of claim 273 wherein classifying the state of the environment comprises determining a map of the environment.

279. The method of claim 273 wherein the operational control parameter is configured to control navigation within the environment.

280. The method of claim 273 wherein the operational control parameter is configured to control interactions with humans or objects in the environment.

281. An integrated chipset, comprising: a robotic control circuit configured to control a set of functions for a set of robots; a data collection circuit configured to handle data from a set of loT devices; a neural network classifier configured to process the data from the loT devices to output a classification of a state of an environment wherein the robots operate; and a neural network control circuit configured to operate on the output of the neural network classifier to output an operational control parameter to the robotic control circuit, wherein the robotic control circuit, the data collection circuit, the neural network classifier and the neural network control circuit are integrated on a high numerical aperture, extreme ultraviolet optical chipset.

282. The integrated chipset of claim 281 wherein the set of robots comprises a plurality of robots, wherein the integrated chipset is on board a robot that manages the plurality of robots.

283. The integrated chipset of claim 281 wherein the set of robots comprises a plurality of robots, wherein the integrated chipset is on board a platform that manages the plurality of robots.

284. The integrated chipset of claim 281 wherein the neural network classifier is configured to recognize objects within the environment.

285. The integrated chipset of claim 281 wherein the neural network classifier is configured to perform scene understanding.

286. The integrated chipset of claim 281 wherein the neural network classifier is configured to determine an activity of a human within the environment.

287. The integrated chipset of claim 281 wherein the neural network classifier is configured to recognize sounds in the environment.

288. The integrated chipset of claim 281 wherein the neural network classifier is configured to determine a pose of at least one robot of the set of robots.

289. The integrated chipset of claim 281 wherein the neural network classifier is configured to determine a map of the environment.

290. The integrated chipset of claim 281 wherein the neural network control circuit is configured to control navigation within the environment.

291. The integrated chipset of claim 281 wherein the neural network control circuit is configured to control interactions with objects in the environment.

292. The integrated chipset of claim 281 wherein the neural network control circuit is configured to control interactions with humans in the environment.

293. A method performed by an integrated chipset, the method comprising: controlling, by a robotic control circuit of the integrated chipset, a set of functions for a set of robots; collecting, by a data collection circuit of the integrated chipset, data from a set of loT devices;processing, by a neural network classifier of the integrated chipset, the data from the loT devices to output a classification of a state of an environment wherein the robots operate; and processing, by a neural network control circuit of the integrated chipset, the output of the neural network classifier to output an operational control parameter to the robotic control circuit, wherein the robotic control circuit, the data collection circuit, the neural network classifier and the neural network control circuit are integrated on a high numerical aperture, extreme ultraviolet optical chipset.

294. The method of claim 293 wherein the set of robots comprises a plurality of robots, wherein the integrated chipset is on board a robot that manages the plurality of robots.

295. The method of claim 293 wherein the set of robots comprises a plurality of robots, wherein the integrated chipset is on board a platform that manages the plurality of robots.

296. The method of claim 293 wherein classifying the state of the environment comprises recognizing objects within the environment.

297. The method of claim 293 wherein classifying the state of the environment comprises determining a pose of at least one robot of the set of robots.

298. The method of claim 293 wherein classifying the state of the environment comprises determining a map of the environment.

299. The method of claim 293 wherein the operational control parameter is configured to control navigation within the environment.

300. The method of claim 293 wherein the operational control parameter is configured to control interactions with humans or objects in the environment.

301. An integrated chipset, comprising: a robotic control circuit configured to control a set of functions for a set of robots; a data collection circuit configured to handle data from a set of loT devices; a neural network classifier configured to process the data from the loT devices to output a classification of a state of an environment wherein the robots operate; and a neural network control circuit configured to operate on the output of the neural network classifier to output an operational control parameter to the robotic control circuit, wherein the robotic control circuit, the data collection circuit, the neural network classifier and the neural network control circuit are integrated on a chipset using a set of gate- all-around field effect transistors.

302. The integrated chipset of claim 301 wherein the set of robots comprises a plurality of robots, wherein the integrated chipset is on board a robot that manages the plurality of robots.

303. The integrated chipset of claim 301 wherein the set of robots comprises a plurality of robots, wherein the integrated chipset is on board a platform that manages the plurality of robots.

304. The integrated chipset of claim 301 wherein the neural network classifier is configured to recognize objects within the environment.

305. The integrated chipset of claim 301 wherein the neural network classifier is configured to perform scene understanding.

306. The integrated chipset of claim 301 wherein the neural network classifier is configured to determine an activity of a human within the environment.

307. The integrated chipset of claim 301 wherein the neural network classifier is configured to recognize sounds in the environment.

308. The integrated chipset of claim 301 wherein the neural network classifier is configured to determine a pose of at least one robot of the set of robots.

309. The integrated chipset of claim 301 wherein the neural network classifier is configured to determine a map of the environment.

310. The integrated chipset of claim 301 wherein the neural network control circuit is configured to control navigation within the environment.

311. The integrated chipset of claim 301 wherein the neural network control circuit is configured to control interactions with objects in the environment.

312. The integrated chipset of claim 301 wherein the neural network control circuit is configured to control interactions with humans in the environment.

313. A method performed by an integrated chipset, the method comprising: controlling, by a robotic control circuit of the integrated chipset, a set of functions for a set of robots; collecting, by a data collection circuit of the integrated chipset, data from a set of loT devices; processing, by a neural network classifier of the integrated chipset, the data from the loT devices to output a classification of a state of an environment wherein the robots operate; and processing, by a neural network control circuit of the integrated chipset, the output of the neural network classifier to output an operational control parameter to the robotic control circuit, wherein the robotic control circuit, the data collection circuit, the neural network classifier and the neural network control circuit are integrated on a chipset using a set of gate- all-around field effect transistors.

314. The method of claim 313 wherein the set of robots comprises a plurality of robots, wherein the integrated chipset is on board a robot that manages the plurality of robots.

315. The method of claim 313 wherein the set of robots comprises a plurality of robots, wherein the integrated chipset is on board a platform that manages the plurality of robots.

316. The method of claim 313 wherein classifying the state of the environment comprises recognizing objects within the environment.

317. The method of claim 313 wherein classifying the state of the environment comprises determining a pose of at least one robot of the set of robots.

318. The method of claim 313 wherein classifying the state of the environment comprises determining a map of the environment.

319. The method of claim 313 wherein the operational control parameter is configured to control navigation within the environment.

320. The method of claim 313 wherein the operational control parameter is configured to control interactions with humans or objects in the environment.

321. An integrated chipset, comprising: a robotic control circuit configured to control a set of functions for a set of robots; a data collection circuit configured to handle data from a set of loT devices; a neural network classifier configured to process the data from the loT devices to output a classification of a state of an environment wherein the robots operate; and a neural network control circuit configured to operate on the output of the neural network classifier to output an operational control parameter to the robotic control circuit, wherein the robotic control circuit, the data collection circuit, the neural network classifier and the neural network control circuit are integrated on a chipset using a set of gate- all-around nanowire field effect transistors.

322. The integrated chipset of claim 321 wherein the set of robots comprises a plurality of robots, wherein the integrated chipset is on board a robot that manages the plurality of robots.

323. The integrated chipset of claim 321 wherein the set of robots comprises a plurality of robots, wherein the integrated chipset is on board a platform that manages the plurality of robots.

324. The integrated chipset of claim 321 wherein the neural network classifier is configured to recognize objects within the environment.

325. The integrated chipset of claim 321 wherein the neural network classifier is configured to perform scene understanding.

326. The integrated chipset of claim 321 wherein the neural network classifier is configured to determine an activity of a human within the environment.

327. The integrated chipset of claim 321 wherein the neural network classifier is configured to recognize sounds in the environment.

328. The integrated chipset of claim 321 wherein the neural network classifier is configured to determine a pose of at least one robot of the set of robots.

329. The integrated chipset of claim 321 wherein the neural network classifier is configured to determine a map of the environment.

330. The integrated chipset of claim 321 wherein the neural network control circuit is configured to control navigation within the environment.

331. The integrated chipset of claim 321 wherein the neural network control circuit is configured to control interactions with objects in the environment.

332. The integrated chipset of claim 321 wherein the neural network control circuit is configured to control interactions with humans in the environment.

333. A method performed by an integrated chipset, the method comprising: controlling, by a robotic control circuit of the integrated chipset, a set of functions for a set of robots; collecting, by a data collection circuit of the integrated chipset, data from a set of loT devices; processing, by a neural network classifier of the integrated chipset, the data from the loT devices to output a classification of a state of an environment wherein the robots operate; andprocessing, by a neural network control circuit of the integrated chipset, the output of the neural network classifier to output an operational control parameter to the robotic control circuit, wherein the robotic control circuit, the data collection circuit, the neural network classifier and the neural network control circuit are integrated on a chipset using a set of gate- all-around nanowire field effect transistors.

334. The method of claim 333 wherein the set of robots comprises a plurality of robots, wherein the integrated chipset is on board a robot that manages the plurality of robots.

335. The method of claim 333 wherein the set of robots comprises a plurality of robots, wherein the integrated chipset is on board a platform that manages the plurality of robots.

336. The method of claim 333 wherein classifying the state of the environment comprises recognizing objects within the environment.

337. The method of claim 333 wherein classifying the state of the environment comprises determining a pose of at least one robot of the set of robots.

338. The method of claim 333 wherein classifying the state of the environment comprises determining a map of the environment.

339. The method of claim 333 wherein the operational control parameter is configured to control navigation within the environment.

340. The method of claim 333 wherein the operational control parameter is configured to control interactions with humans or objects in the environment.

341. An integrated chipset, comprising: a robotic control circuit configured to control a set of functions for a set of robots; a data collection circuit configured to handle data from a set of loT devices; a neural network classifier configured to process the data from the loT devices to output a classification of a state of an environment wherein the robots operate; and a neural network control circuit configured to operate on the output of the neural network classifier to output an operational control parameter to the robotic control circuit, wherein the robotic control circuit, the data collection circuit, the neural network classifier and the neural network control circuit are integrated on a chipset using a set of gate- all-around nanosheet field effect transistors.

342. The integrated chipset of claim 341 wherein the set of robots comprises a plurality of robots, wherein the integrated chipset is on board a robot that manages the plurality of robots.

343. The integrated chipset of claim 341 wherein the set of robots comprises a plurality of robots, wherein the integrated chipset is on board a platform that manages the plurality of robots.

344. The integrated chipset of claim 341 wherein the neural network classifier is configured to recognize objects within the environment.

345. The integrated chipset of claim 341 wherein the neural network classifier is configured to perform scene understanding.

346. The integrated chipset of claim 341 wherein the neural network classifier is configured to determine an activity of a human within the environment.

347. The integrated chipset of claim 341 wherein the neural network classifier is configured to recognize sounds in the environment.

348. The integrated chipset of claim 341 wherein the neural network classifier is configured to determine a pose of at least one robot of the set of robots.

349. The integrated chipset of claim 341 wherein the neural network classifier is configured to determine a map of the environment.

350. The integrated chipset of claim 341 wherein the neural network control circuit is configured to control navigation within the environment.

351. The integrated chipset of claim 341 wherein the neural network control circuit is configured to control interactions with objects in the environment.

352. The integrated chipset of claim 341 wherein the neural network control circuit is configured to control interactions with humans in the environment.

353. A method performed by an integrated chipset, the method comprising: controlling, by a robotic control circuit of the integrated chipset, a set of functions for a set of robots; collecting, by a data collection circuit of the integrated chipset, data from a set of loT devices; processing, by a neural network classifier of the integrated chipset, the data from the loT devices to output a classification of a state of an environment wherein the robots operate; and processing, by a neural network control circuit of the integrated chipset, the output of the neural network classifier to output an operational control parameter to the robotic control circuit, wherein the robotic control circuit, the data collection circuit, the neural network classifier and the neural network control circuit are integrated on a chipset using a set of gate- all-around nanosheet field effect transistors.

354. The method of claim 353 wherein the set of robots comprises a plurality of robots, wherein the integrated chipset is on board a robot that manages the plurality of robots.

355. The method of claim 353 wherein the set of robots comprises a plurality of robots, wherein the integrated chipset is on board a platform that manages the plurality of robots.

356. The method of claim 353 wherein classifying the state of the environment comprises recognizing objects within the environment.

357. The method of claim 353 wherein classifying the state of the environment comprises determining a pose of at least one robot of the set of robots.

358. The method of claim 353 wherein classifying the state of the environment comprises determining a map of the environment.

359. The method of claim 353 wherein the operational control parameter is configured to control navigation within the environment.

360. The method of claim 353 wherein the operational control parameter is configured to control interactions with humans or objects in the environment.

361. An integrated chipset, comprising: a robotic control circuit configured to control a set of functions for a set of robots;a data collection circuit configured to handle data from a set of loT devices; a neural network classifier configured to process the data from the loT devices to output a classification of a state of an environment wherein the robots operate; and a neural network control circuit configured to operate on the output of the neural network classifier to output an operational control parameter to the robotic control circuit, wherein the robotic control circuit, the data collection circuit, the neural network classifier and the neural network control circuit are integrated on a chipset using a set of gate- all-around complementary field effect transistors.

362. The integrated chipset of claim 361 wherein the set of robots comprises a plurality of robots, wherein the integrated chipset is on board a robot that manages the plurality of robots.

363. The integrated chipset of claim 361 wherein the set of robots comprises a plurality of robots, wherein the integrated chipset is on board a platform that manages the plurality of robots.

364. The integrated chipset of claim 361 wherein the neural network classifier is configured to recognize objects within the environment.

365. The integrated chipset of claim 361 wherein the neural network classifier is configured to perform scene understanding.

366. The integrated chipset of claim 361 wherein the neural network classifier is configured to determine an activity of a human within the environment.

367. The integrated chipset of claim 361 wherein the neural network classifier is configured to recognize sounds in the environment.

368. The integrated chipset of claim 361 wherein the neural network classifier is configured to determine a pose of at least one robot of the set of robots.

369. The integrated chipset of claim 361 wherein the neural network classifier is configured to determine a map of the environment.

370. The integrated chipset of claim 361 wherein the neural network control circuit is configured to control navigation within the environment.

371. The integrated chipset of claim 361 wherein the neural network control circuit is configured to control interactions with objects in the environment.

372. The integrated chipset of claim 361 wherein the neural network control circuit is configured to control interactions with humans in the environment.

373. A method performed by an integrated chipset, the method comprising: controlling, by a robotic control circuit of the integrated chipset, a set of functions for a set of robots; collecting, by a data collection circuit of the integrated chipset, data from a set of loT devices; processing, by a neural network classifier of the integrated chipset, the data from the loT devices to output a classification of a state of an environment wherein the robots operate; and processing, by a neural network control circuit of the integrated chipset, the output of the neural network classifier to output an operational control parameter to the robotic control circuit,wherein the robotic control circuit, the data collection circuit, the neural network classifier and the neural network control circuit are integrated on a chipset using a set of gate- all-around complementary field effect transistors.

374. The method of claim 373 wherein the set of robots comprises a plurality of robots, wherein the integrated chipset is on board a robot that manages the plurality of robots.

375. The method of claim 373 wherein the set of robots comprises a plurality of robots, wherein the integrated chipset is on board a platform that manages the plurality of robots.

376. The method of claim 373 wherein classifying the state of the environment comprises recognizing objects within the environment.

377. The method of claim 373 wherein classifying the state of the environment comprises determining a pose of at least one robot of the set of robots.

378. The method of claim 373 wherein classifying the state of the environment comprises determining a map of the environment.

379. The method of claim 373 wherein the operational control parameter is configured to control navigation within the environment.

380. The method of claim 373 wherein the operational control parameter is configured to control interactions with humans or objects in the environment.

381. An integrated chipset, comprising: a robotic control circuit configured to control a set of functions for a set of robots; a data collection circuit configured to handle data from a set of loT devices; a neural network classifier configured to process the data from the loT devices to output a classification of a state of an environment wherein the robots operate; and a neural network control circuit configured to operate on the output of the neural network classifier to output an operational control parameter to the robotic control circuit, wherein the robotic control circuit, the data collection circuit, the neural network classifier and the neural network control circuit are integrated on a carbon nanotube chipset.

382. The integrated chipset of claim 381 wherein the set of robots comprises a plurality of robots, wherein the integrated chipset is on board a robot that manages the plurality of robots.

383. The integrated chipset of claim 381 wherein the set of robots comprises a plurality of robots, wherein the integrated chipset is on board a platform that manages the plurality of robots.

384. The integrated chipset of claim 381 wherein the neural network classifier is configured to recognize objects within the environment.

385. The integrated chipset of claim 381 wherein the neural network classifier is configured to perform scene understanding.

386. The integrated chipset of claim 381 wherein the neural network classifier is configured to determine an activity of a human within the environment.

387. The integrated chipset of claim 381 wherein the neural network classifier is configured to recognize sounds in the environment.

388. The integrated chipset of claim 381 wherein the neural network classifier is configured to determine a pose of at least one robot of the set of robots.

389. The integrated chipset of claim 381 wherein the neural network classifier is configured to determine a map of the environment.

390. The integrated chipset of claim 381 wherein the neural network control circuit is configured to control navigation within the environment.

391. The integrated chipset of claim 381 wherein the neural network control circuit is configured to control interactions with objects in the environment.

392. The integrated chipset of claim 381 wherein the neural network control circuit is configured to control interactions with humans in the environment.

393. A method performed by an integrated chipset, the method comprising: controlling, by a robotic control circuit of the integrated chipset, a set of functions for a set of robots; collecting, by a data collection circuit of the integrated chipset, data from a set of loT devices; processing, by a neural network classifier of the integrated chipset, the data from the loT devices to output a classification of a state of an environment wherein the robots operate; and processing, by a neural network control circuit of the integrated chipset, the output of the neural network classifier to output an operational control parameter to the robotic control circuit, wherein the robotic control circuit, the data collection circuit, the neural network classifier and the neural network control circuit are integrated on a carbon nanotube chipset.

394. The method of claim 393 wherein the set of robots comprises a plurality of robots, wherein the integrated chipset is on board a robot that manages the plurality of robots.

395. The method of claim 393 wherein the set of robots comprises a plurality of robots, wherein the integrated chipset is on board a platform that manages the plurality of robots.

396. The method of claim 393 wherein classifying the state of the environment comprises recognizing objects within the environment.

397. The method of claim 393 wherein classifying the state of the environment comprises determining a pose of at least one robot of the set of robots.

398. The method of claim 393 wherein classifying the state of the environment comprises determining a map of the environment.

399. The method of claim 393 wherein the operational control parameter is configured to control navigation within the environment.

400. The method of claim 393 wherein the operational control parameter is configured to control interactions with humans or objects in the environment.

401. An integrated chipset, comprising: a robotic control circuit configured to control a set of functions for a set of robots; a data collection circuit configured to handle data from a set of loT devices; a neural network classifier configured to process the data from the loT devices to output a classification of a state of an environment wherein the robots operate; and a neural network control circuit configured to operate on the output of the neural network classifier to output an operational control parameter to the robotic control circuit,wherein the robotic control circuit, the data collection circuit, the neural network classifier and the neural network control circuit are integrated on a chipset having high- bandwidth SRAM memory.

402. The integrated chipset of claim 401 wherein the set of robots comprises a plurality of robots, wherein the integrated chipset is on board a robot that manages the plurality of robots.

403. The integrated chipset of claim 401 wherein the set of robots comprises a plurality of robots, wherein the integrated chipset is on board a platform that manages the plurality of robots.

404. The integrated chipset of claim 401 wherein the neural network classifier is configured to recognize objects within the environment.

405. The integrated chipset of claim 401 wherein the neural network classifier is configured to perform scene understanding.

406. The integrated chipset of claim 401 wherein the neural network classifier is configured to determine an activity of a human within the environment.

407. The integrated chipset of claim 401 wherein the neural network classifier is configured to recognize sounds in the environment.

408. The integrated chipset of claim 401 wherein the neural network classifier is configured to determine a pose of at least one robot of the set of robots.

409. The integrated chipset of claim 401 wherein the neural network classifier is configured to determine a map of the environment.

410. The integrated chipset of claim 401 wherein the neural network control circuit is configured to control navigation within the environment.

411. The integrated chipset of claim 401 wherein the neural network control circuit is configured to control interactions with objects in the environment.

412. The integrated chipset of claim 401 wherein the neural network control circuit is configured to control interactions with humans in the environment.

413. A method performed by an integrated chipset, the method comprising: controlling, by a robotic control circuit of the integrated chipset, a set of functions for a set of robots; collecting, by a data collection circuit of the integrated chipset, data from a set of loT devices; processing, by a neural network classifier of the integrated chipset, the data from the loT devices to output a classification of a state of an environment wherein the robots operate; and processing, by a neural network control circuit of the integrated chipset, the output of the neural network classifier circuit to output an operational control parameter to the robotic control circuit, wherein the robotic control circuit, the data collection circuit, the neural network classifier and the neural network control circuit are integrated on a chipset having high- bandwidth SRAM memory.

414. The method of claim 413 wherein the set of robots comprises a plurality of robots, wherein the integrated chipset is on board a robot that manages the plurality of robots.

415. The method of claim 413 wherein the set of robots comprises a plurality of robots, wherein the integrated chipset is on board a platform that manages the plurality of robots.

416. The method of claim 413 wherein classifying the state of the environment comprises recognizing objects within the environment.

417. The method of claim 413 wherein classifying the state of the environment comprises determining a pose of at least one robot of the set of robots.

418. The method of claim 413 wherein classifying the state of the environment comprises determining a map of the environment.

419. The method of claim 413 wherein the operational control parameter is configured to control navigation within the environment.

420. The method of claim 413 wherein the operational control parameter is configured to control interactions with humans or objects in the environment.

421. An integrated chipset, comprising: a robotic control circuit configured to control a set of functions for a set of robots; a data collection circuit configured to handle data from a set of loT devices; a neural network classifier configured to process the data from the loT devices to output a classification of a state of an environment wherein the robots operate; and a neural network control circuit configured to operate on the output of the neural network classifier to output an operational control parameter to the robotic control circuit, wherein the robotic control circuit, the data collection circuit, the neural network classifier and the neural network control circuit are integrated on a chipset having 3D-NAND flash memory.

422. The integrated chipset of claim 421 wherein the set of robots comprises a plurality of robots, wherein the integrated chipset is on board a robot that manages the plurality of robots.

423. The integrated chipset of claim 421 wherein the set of robots comprises a plurality of robots, wherein the integrated chipset is on board a platform that manages the plurality of robots.

424. The integrated chipset of claim 421 wherein the neural network classifier is configured to recognize objects within the environment.

425. The integrated chipset of claim 421 wherein the neural network classifier is configured to perform scene understanding.

426. The integrated chipset of claim 421 wherein the neural network classifier is configured to determine an activity of a human within the environment.

427. The integrated chipset of claim 421 wherein the neural network classifier is configured to recognize sounds in the environment.

428. The integrated chipset of claim 421 wherein the neural network classifier is configured to determine a pose of at least one robot of the set of robots.

429. The integrated chipset of claim 421 wherein the neural network classifier is configured to determine a map of the environment.

430. The integrated chipset of claim 421 wherein the neural network control circuit is configured to control navigation within the environment.

431. The integrated chipset of claim 421 wherein the neural network control circuit is configured to control interactions with objects in the environment.

432. The integrated chipset of claim 421 wherein the neural network control circuit is configured to control interactions with humans in the environment.

433. A method performed by an integrated chipset, the method comprising: controlling, by a robotic control circuit of the integrated chipset, a set of functions for a set of robots; collecting, by a data collection circuit of the integrated chipset, data from a set of loT devices; processing, by a neural network classifier of the integrated chipset, the data from the loT devices to output a classification of a state of an environment wherein the robots operate; and processing, by a neural network control circuit of the integrated chipset, the output of the neural network classifier to output an operational control parameter to the robotic control circuit, wherein the robotic control circuit, the data collection circuit, the neural network classifier and the neural network control circuit are integrated on a chipset having 3D-NAND flash memory.

434. The method of claim 433 wherein the set of robots comprises a plurality of robots, wherein the integrated chipset is on board a robot that manages the plurality of robots.

435. The method of claim 433 wherein the set of robots comprises a plurality of robots, wherein the integrated chipset is on board a platform that manages the plurality of robots.

436. The method of claim 433 wherein classifying the state of the environment comprises recognizing objects within the environment.

437. The method of claim 433 wherein classifying the state of the environment comprises determining a pose of at least one robot of the set of robots.

438. The method of claim 433 wherein classifying the state of the environment comprises determining a map of the environment.

439. The method of claim 433 wherein the operational control parameter is configured to control navigation within the environment.

440. The method of claim 433 wherein the operational control parameter is configured to control interactions with humans or objects in the environment.

441. An integrated chipset, comprising: a robotic control circuit configured to control a set of functions for a set of robots; a data collection circuit configured to handle data from a set of loT devices; a neural network classifier configured to process the data from the loT devices to output a classification of a state of an environment wherein the robots operate; and a neural network control circuit configured to operate on the output of the neural network classifier to output an operational control parameter to the robotic control circuit, wherein the robotic control circuit, the data collection circuit, the neural network classifier and the neural network control circuit are integrated on a hybrid-bonded chipset.

442. The integrated chipset of claim 441 wherein the set of robots comprises a plurality of robots, wherein the integrated chipset is on board a robot that manages the plurality of robots.

443. The integrated chipset of claim 441 wherein the set of robots comprises a plurality of robots, wherein the integrated chipset is on board a platform that manages the plurality of robots.

444. The integrated chipset of claim 441 wherein the neural network classifier is configured to recognize objects within the environment.

445. The integrated chipset of claim 441 wherein the neural network classifier is configured to perform scene understanding.

446. The integrated chipset of claim 441 wherein the neural network classifier is configured to determine an activity of a human within the environment.

447. The integrated chipset of claim 441 wherein the neural network classifier is configured to recognize sounds in the environment.

448. The integrated chipset of claim 441 wherein the neural network classifier is configured to determine a pose of at least one robot of the set of robots.

449. The integrated chipset of claim 441 wherein the neural network classifier is configured to determine a map of the environment.

450. The integrated chipset of claim 441 wherein the neural network control circuit is configured to control navigation within the environment.

451. The integrated chipset of claim 441 wherein the neural network control circuit is configured to control interactions with objects in the environment.

452. The integrated chipset of claim 441 wherein the neural network control circuit is configured to control interactions with humans in the environment.

453. A method performed by an integrated chipset, the method comprising: controlling, by a robotic control circuit of the integrated chipset, a set of functions for a set of robots; collecting, by a data collection circuit of the integrated chipset, data from a set of loT devices; processing, by a neural network classifier of the integrated chipset, the data from the loT devices to output a classification of a state of an environment wherein the robots operate; and processing, by a neural network control circuit of the integrated chipset, the output of the neural network classifier to output an operational control parameter to the robotic control circuit, wherein the robotic control circuit, the data collection circuit, the neural network classifier and the neural network control circuit are integrated on a hybrid-bonded chipset.

454. The method of claim 453 wherein the set of robots comprises a plurality of robots, wherein the integrated chipset is on board a robot that manages the plurality of robots.

455. The method of claim 453 wherein the set of robots comprises a plurality of robots, wherein the integrated chipset is on board a platform that manages the plurality of robots.

456. The method of claim 453 wherein classifying the state of the environment comprises recognizing objects within the environment.

457. The method of claim 453 wherein classifying the state of the environment comprises determining a pose of at least one robot of the set of robots.

458. The method of claim 453 wherein classifying the state of the environment comprises determining a map of the environment.

459. The method of claim 453 wherein the operational control parameter is configured to control navigation within the environment.

460. The method of claim 453 wherein the operational control parameter is configured to control interactions with humans or objects in the environment.

461. A robotic fleet comprising: a plurality of robotic systems, wherein each robotic system of the plurality of robotic systems comprises an integrated chipset and a set of sensors, wherein each integrated chipset is configured to generate synthetic training data; and a robotic system in communication with at least a subset of the plurality of robotic systems, wherein the robotic system comprises an integrated chipset configured to perform steps including: measuring a performance of an Al model being executed by the integrated chipset; determining that additional training data is required to optimize the performance of the Al model; transmitting, to the subset of the plurality of robotic systems, a request for training data, wherein the request specifies one or more context factors for the robotic system; receiving synthetic training data generated by the subset of the plurality of robotic systems; and fine-tuning the Al model using the synthetic training data to optimize the performance of the Al model.

462. The robotic fleet of claim 461 wherein the subset of the plurality of robotic systems generate the synthetic training data using an environment digital twin and the one or more context factors.

463. The robotic fleet of claim 461 wherein the subset of the plurality of robotic systems generate the synthetic training data using a plurality of digital twins.

464. The robotic fleet of claim 461 wherein the subset of the plurality of robotic systems generate the synthetic training data using generative Al models.

465. The robotic fleet of claim 464 wherein the generative Al models comprise one or more of generative adversarial networks (GANs) or large language models (LLMs).

466. The robotic fleet of claim 461 wherein a first robotic system of the plurality of robotic systems generates a first portion of the synthetic training data based on a first set of sensors on board the first robotic system and a second robotic system of the plurality of robotic systems generates a second portion of the synthetic training data based on a second set of sensors on board the second robotic system, wherein the first set of sensors and the second set of sensors include different sensors.

467. The robotic fleet of claim 461 wherein a first robotic system of the plurality of robotic systems generates a first portion of the synthetic training data using a first Al model and a second robotic system of the plurality of robotic systems generates a second portion of the synthetic training data using a second Al model, wherein the first Al model and second Al model are different types of Al models.

468. The robotic fleet of claim 461 wherein the synthetic training data comprises data captured by the set of sensors.

469. The robotic fleet of claim 461 wherein the one or more context factors comprise a task being performed by the robotic system.

470. The robotic fleet of claim 469 wherein the subset of the plurality of robotic systems generates the synthetic training data by simulating performance of the task.

471. The robotic fleet of claim 470 wherein measuring the performance of the Al model comprises measuring the performance of the Al model for the task being performed by the robotic system.

472. The robotic fleet of claim 461 wherein the one or more context factors describe a local environment of the robotic system.

473. The robotic fleet of claim 472 wherein the one or more context factors describe one or more humans nearby the robotic system.

474. The robotic fleet of claim 472 wherein the one or more context factors describe one or more devices nearby the robotic system.

475. The robotic fleet of claim 472 wherein the subset of the plurality of robotic systems generates the synthetic training data by simulating the local environment of the robotic system.

476. The robotic fleet of claim 472 wherein measuring the performance of the Al model comprises measuring the performance of the Al model with respect to the local environment of the robotic system.

477. The robotic fleet of claim 461 wherein the Al model is one of a navigation model, an object manipulation model, a language model, or a network optimization model.

478. The robotic fleet of claim 461 wherein the Al model is multimodal, wherein the synthetic training data comprises one or more of audio data, image data, or video data.

479. The robotic fleet of claim 461 wherein the robotic system comprises a network enhancement chipset that optimizes the communication with the subset of the plurality of robotic systems.

480. The robotic fleet of claim 461 wherein the integrated chipset comprises a plurality of processing units integrated on a single substrate.

481. A robotic system comprising: an integrated chipset; and an intelligence layer configured to perform steps including: determining a task for performance by the integrated chipset of the robotic system, wherein the integrated chipset comprises a plurality of processing units;selecting an Al model for execution by the integrated chipset for performing the task; obtaining training data for optimizing the Al model’s performance on the determined task; causing the integrated chipset to retrain the Al model using the training data; and causing the integrated chipset to execute the retrained Al model to perform the task;482. The robotic system of claim 481 wherein obtaining the training data comprises retrieving data from storage on board the robotic system.

483. The robotic system of claim 481 wherein obtaining the training data comprises causing the integrated chipset to generate synthetic training data.

484. The robotic system of claim 483 wherein the integrated chipset generates the synthetic training data using an environment digital twin and one or more context factors for the robotic system.

485. The robotic system of claim 484 wherein the one or more context factors comprise one or more requirements, objectives, or methods of performing the task.

486. The robotic system of claim 484 wherein the one or more context factors describe one or more humans nearby the robotic system.

487. The robotic system of claim 484 wherein the one or more context factors describe one or more devices nearby the robotic system.

488. The robotic system of claim 483 wherein the integrated chipset generates the synthetic training data by simulating the performance of the task.

489. The robotic system of claim 483 wherein the integrated chipset generates the synthetic training data by: transmitting a request for training data to a plurality of other robotic systems in communication with the robotic system; and receiving the synthetic training data from the other robotic systems.

490. The robotic system of claim 489 wherein the synthetic training data comprises data captured by sensors of the other robotic systems.

491. The robotic system of claim 489 wherein the synthetic training data is generated based on simulations performed by the other robotic systems.

492. The robotic system of claim 481 wherein causing the integrated chipset to retrain the Al model using the training data comprises: selecting a most optimal processing unit of the integrated chipset for retraining; and causing the most optimal processing unit to retrain the Al model using the training data.

493. The robotic system of claim 492 wherein selecting the most optimal processing unit is based on one or more of processing capability or power efficiency of each processing unit.

494. The robotic system of claim 492 wherein the most optimal processing unit is an FPGA, wherein causing the most optimal processing unit to retrain the Al model using the training data comprises reprogramming the FPGA to execute the retraining.

495. The robotic system of claim 492 wherein the intelligence layer is further configured to perform steps comprising: determining that a context for performing the task has ended; and deleting the retrained Al model.

496. The robotic system of claim 481 wherein the intelligence layer is further configured to perform steps comprising: measuring the performance of the task; updating the training data in real-time based on the measured performance of the task; and causing the integrated chipset to further retrain the Al model using the updated training data.

497. The robotic system of claim 481 wherein the Al model is one of a navigation model, an object manipulation model, a language model, or a network optimization model.

498. The robotic system of claim 481 wherein the Al model is multimodal, wherein the training data comprises one or more of audio data, image data, or video data.

499. The robotic system of claim 481 wherein the plurality of processing units is integrated on a single substrate.

500. The robotic system of claim 499 wherein the plurality of processing units is manufactured on a single silicon wafer.

501. A robotic fleet comprising: a fleet management platform configured to assign a plurality of roles to a plurality of robotic systems of the robotic fleet; and a robotic system in communication with the fleet management platform, wherein the robotic system comprises an integrated chipset comprising a plurality of processing units, wherein the robotic system is configured to perform steps including: receiving a role assignment from the fleet management platform; configuring the plurality of processing units to perform tasks associated with the role; determining a next task from a task queue for performance by the robotic system; assigning the next task to a configured processing unit based on one or more context factors; and executing the next task using the configured processing unit.

502. The robotic fleet of claim 501 wherein the fleet management platform is configured to dynamically re-assign the plurality of roles among the plurality of robotic systems of the robotic fleet based on a current state of the robotic fleet.

503. The robotic fleet of claim 502 wherein the current state of the robotic fleet comprises a status of each robotic system of the plurality of robotic systems.

504. The robotic fleet of claim 503 wherein the status of each robotic system comprises one or more of a power level of each robotic system, an availability to perform additional tasks of each robotic system, and a processing capability of each robotic system.

505. The robotic fleet of claim 502 wherein the current state of the robotic fleet comprises an environment of each robotic system.

506. The robotic fleet of claim 502 wherein the current state of the robotic fleet comprises a set of tasks assigned to the robotic fleet.

507. The robotic fleet of claim 501 wherein the role assignment is one or more of an enhanced vision role, a data analysis role, or a decision-making role.

508. The robotic fleet of claim 501 wherein the fleet management platform is onboard a controller robotic system.

509. The robotic fleet of claim 508 wherein the controller robotic system is configured to assign a fleet management role to another robotic system of the robotic fleet.

510. The robotic fleet of claim 501 wherein the context factors comprise an Al model used to perform the task.

511. The robotic fleet of claim 501 wherein the context factors comprise a power efficiency of the configured processing unit.

512. The robotic fleet of claim 501 wherein the context factors comprise a parallelizability of the next task.

513. The robotic fleet of claim 501 wherein the context factors comprise one or more timing requirements for the next task.

514. The robotic fleet of claim 501 wherein the context factors comprise a communication speed of the configured processing unit.

515. The robotic fleet of claim 501 wherein the robotic system is further configured to perform steps comprising, responsive to receiving the role assignment, requesting an Al model associated with the role from another device in communication with the robotic system.

516. The robotic fleet of claim 501 wherein configuring the plurality of processing units to perform tasks associated with the role comprises assigning one or more Al models to one or more processing units.

517. The robotic fleet of claim 516 wherein assigning one or more Al models to one or more processing units comprises reprogramming an FPGA to execute an assigned Al model.

518. The robotic system of claim 516 wherein the one or more Al models comprise a navigation model, an object manipulation model, a language model, or a network optimization model.

519. The robotic system of claim 501 wherein the plurality of processing units is integrated on a single substrate.

520. The robotic system of claim 519 wherein the plurality of processing units is manufactured on a single silicon wafer.

521. A robotic system comprising: an integrated chipset comprising a plurality of processing units; and an intelligence layer configured to perform steps including: receiving a role assignment from a fleet management platform;configuring the plurality of processing units to perform tasks associated with the role; determining a next task from a task queue for performance by the robotic system; selecting an algorithm for execution by the integrated chipset for performing subtask generation; causing the integrated chipset to execute the selected algorithm to generate one or more sub-tasks for performance of the next task; assigning the one or more sub-tasks to the plurality of processing units; and causing the plurality of processing units of the integrated chipset to execute the one or more sub-tasks to perform the next task.

522. The robotic system of claim 521 wherein the selected algorithm for performing sub-task generation comprises reinforcement learning based on simulating actions within a digital twin environment523. The robotic system of claim 521 wherein the selected algorithm for performing sub-task generation comprises one or more of a goal decomposition algorithm, a hierarchical task network, or a genetic algorithm.

524. The robotic system of claim 521 wherein selecting the algorithm comprises selecting an algorithm that is most suitable for sub-task generation based on the next task.

525. The robotic system of claim 521 wherein selecting the algorithm comprises: selecting multiple algorithms for parallel generation of sub-tasks based on the next task; and selecting the one or more sub-tasks based on corresponding sub-tasks generated by the multiple algorithms.

526. The robotic system of claim 521 wherein the role assignment is one or more of an enhanced vision role, a data analysis role, or a decision-making role.

527. The robotic system of claim 521 wherein the robotic system is further configured to perform steps comprising, responsive to receiving the role assignment, requesting an Al model associated with the role from another device in communication with the robotic system.

528. The robotic system of claim 521 wherein configuring the plurality of processing units to perform tasks associated with the role comprises assigning one or more Al models to one or more of the plurality of processing units.

529. The robotic fleet of claim 528 wherein assigning one or more Al models to one or more of the plurality of processing units comprises reprogramming an FPGA to execute an assigned Al model.

530. The robotic system of claim 528 wherein the one or more Al models comprise a navigation model, an object manipulation model, a language model, or a network optimization model.

531. The robotic system of claim 521 wherein the one or more sub-tasks comprise optimization of a network.

532. The robotic system of claim 521 wherein the one or more sub-tasks comprise applying a plurality of governance frameworks to control the robotic system.

533. The robotic system of claim 521 wherein the one or more sub-tasks comprise a navigation task.

534. The robotic system of claim 521 wherein the next task comprises training an Al model, wherein the one or more sub-tasks comprise generating synthetic data for training the Al model.

535. The robotic system of claim 534 wherein the one or more sub-tasks further comprise requesting synthetic data from other robotic systems in communication with the robotic system.

536. The robotic system of claim 521 wherein the received role assignment is a fleet controller role, wherein the next task comprises dynamically assigning roles to other robotic systems in communication with the robotic system.

537. The robotic system of claim 521 wherein the intelligence layer is further configured to perform steps comprising assigning at least one of the sub-tasks to another robotic system.

538. The robotic system of claim 521 wherein the plurality of processing units is integrated on a single substrate.

539. The robotic system of claim 538 wherein the plurality of processing units is manufactured on a single silicon wafer.

540. The robotic system of claim 521 wherein the plurality of processing units is separately manufactured and bonded to a single substrate.

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