48 results about "Human operator" patented technology

Provided herein is a control system and methods thereof for controlling operation of a robot. The control system comprises: a robot, including a plurality of sensors wherein each generating a stream of raw sensor data having a data type, size, and frequency, and a plurality of actuators cause movement of the robot; an autonomous control subsystem configured to receive the sensor data and output autonomous actuator data; and a teleoperation control subsystem configured to receive the sensor data, transmit the sensor data to a human operator, and output teleoperation actuator control signals, wherein the autonomous control subsystem and the teleoperation control subsystem receive, from the robot, sensor data having the same data type, size, and frequency, and wherein the autonomous actuator data and the teleoperation actuator data have the same data type, size, and frequency; and a control-determining subsystem configured to switch between autonomous and teleoperation control.

An energy-conscious workflow planning system based on LLM with dynamic resource allocation, consisting of: a workflow input interface configured to receive workflow-directed acyclic graphs (DAGs), energy budget constraints, system performance constraints, and natural language requests from human operators; a large language model (LLM) logic module connected to the workflow input interface and configured to analyze the workflow specifications and system constraints in natural language, generate energy-conscious planning recommendations based on the analyzed workflow specifications, and provide explainable planning rationales in natural language; a reinforcement learning-based scheduling unit connected to the LLM reasoning module and configured to: receive scheduling recommendations from the LLM reasoning agent, fine-tune task-resource assignments by dynamically adapting to runtime variations, and perform online resource redistribution under runtime variability; an energy monitoring unit configured to: continuously monitor CPU and GPU utilization in heterogeneous clusters, track power consumption and thermal limits per node, and generate energy profiles for system components; a multi-objective optimization engine configured to: perform a Pareto-optimal scheduling analysis that balances energy consumption, lead time and reliability, apply statistical and AI-supported trade-off analyses and ensure optimal resource allocation based on Pareto frontier analysis; a dynamic resource allocation unit configured to: use predictive models that incorporate LLM inferences and feedback from reinforcement learning, reassign tasks between nodes and clusters while minimizing energy consumption and improving system throughput based on the predictive models; a performance optimization module configured to optimize scheduling decisions using multi-criteria optimization analysis; and a user interface that allows human operators to override and refine planning strategies in real time based on verifiable planning reasons.

Provided herein is a control system and methods thereof for controlling operation of a robot. The control system comprises: a robot, including a plurality of sensors wherein each generating a stream of raw sensor data having a data type, size, and frequency, and a plurality of actuators cause movement of the robot; an autonomous control subsystem configured to receive the sensor data and output autonomous actuator data; and a teleoperation control subsystem configured to receive the sensor data, transmit the sensor data to a human operator, and output teleoperation actuator control signals, wherein the autonomous control subsystem and the teleoperation control subsystem receive, from the robot, sensor data having the same data type, size, and frequency, and wherein the autonomous actuator data and the teleoperation actuator data have the same data type, size, and frequency; and a control-determining subsystem configured to switch between autonomous and teleoperation control.

The invention discloses a multi-objective reinforcement learning man-machine cooperative assembly task allocation method based on neighborhood parameter migration, and the method comprises the steps: building a mathematical model which aims at minimizing the physiological fatigue accumulated value of a human operator and minimizing the maximum completion time for a multi-objective optimization problem of task allocation in a man-machine cooperative assembly system; a multi-target problem is decomposed into N standard sub-problems by adopting a weighting and decomposition strategy, and training is accelerated through a neighborhood parameter migration strategy; each sub-problem is solved based on a near-end strategy optimization algorithm of an Actor-Critic framework, Gaussian noise is added to an Actor network to simulate environment uncertainty, an action mask mechanism is introduced to process priority constraints of assembly tasks, and it is ensured that a generated task allocation scheme is always feasible. According to the method, the convergence speed and diversity of the Pareto solution set can be remarkably improved, and the assembly efficiency and operator fatigue are effectively balanced.

Approaches for initiating a human takeover by a virtual artificially intelligent (AI) agent. A predetermined indication is used as a signal for initiating the human takeover across a variety of contexts. Responsive to detecting the predetermined indication, a human operator is automatically notified to take over the conversation and the AI system is prepared for transferring control to a human operator.

The present invention discloses an operator / UAV cluster hybrid intelligent collaborative control system and method, the system includes an operator trust calculation module, an operator interaction instruction module, a cluster trust calculation module, a distributed collaborative control calculation module, and a visual display module. The method of the present invention is as follows: Step 1: Given a given intelligent agent cluster model and a UAV cluster model and mapping them; Step 2: UAV cluster communication model design; Step 3: UAV cluster distributed collaborative controller design; Step 4: Human-machine mutual trust model design and trust judgment. The present invention considers the impact of external environmental interference on the UAV cluster and introduces the advantages of human operator intelligent decision-making and UAV distributed autonomous control to construct an efficient, flexible, and scalable hybrid intelligent collaborative control framework, and improves the task execution efficiency and safety of the UAV cluster in a dynamic environment.

The invention relates to a time-space dynamic evolution-oriented man-machine cooperation assembly cognitive inference method, which comprises the following steps of: extracting visual features in an assembly scene, generating a scene graph, constructing a time hyperedge, a space hyperedge and a task hyperedge, and fusing the three types of hyperedges to form a hyperedge set; time-varying non-pairwise relationships among human operators, robots, assembly operations and various assembly component nodes are represented, a hyperedge incidence matrix is defined, a man-machine cooperation assembly knowledge space-time hypergraph is constructed, and hyperedge representation among assembly components is realized; designing a stacked graph neural network with a self-excitation characteristic based on a multi-event hokes process, learning high-order task association among assembly nodes, and updating the change of the high-order task association along with time to realize space-time hypergraph representation; and modeling a self-excitation process among different sub-tasks, and capturing individual features and collective association to realize man-machine cooperation assembly. The problem that man-machine cooperation cognition in a time-varying task is difficult to infer is solved, and man-machine cooperation assembly efficiency and initiative are improved.

The present invention belongs to the technical field of unmanned cluster systems, and specifically relates to a large-scale model-based sensing and control method for human-machine hybrid systems. The method inputs cluster status information, images with target borders, target categories, target positioning information, and situation assessment complexity indicators into a multimodal large-scale model. The multimodal large-scale model extracts key information from a large amount of data collected by the unmanned cluster, generates a streamlined natural language situation assessment for a human operator, and the human operator issues cluster control voice instructions that conform to human language habits. The human operator's voice instructions are converted into natural language text through a speech recognition algorithm and input into the multimodal large-scale model. The multimodal large-scale model outputs high-level cluster control instructions that meet the format of the unmanned platform's autonomous control algorithm, enabling the unmanned cluster to take action directly according to the human operator's intentions, thereby improving the perception and control efficiency of the human-machine hybrid system.

An object of a system according to an embodiment is to improve service experience by responding to an inquiry from a customer immediately and on a 24-hour basis.SOLUTION: A system according to an embodiment includes a natural language processing unit, a learning unit, a transfer unit, a translation unit, an aggregation unit, and a feedback analysis unit. The natural language processor uses natural language processing to understand the intent of the customer's question and generate an appropriate answer. The learner evolves to provide optimal answers from past dialogues. The forwarder automatically forwards the complex query to a human operator. The translation unit supports multiple languages, allows a customer to ask a question in his / her native language, and automatically translates the question to provide an answer. The aggregation unit aggregates inquiry leads of a plurality of services into one application. The feedback analyzer automatically analyzes the feedback from the customer and sends it immediately to the product.SELECTED DRAWING: Figure 1

The present invention relates to a method, a computer program with instructions, and devices for controlling a means of transportation. In a first step, a situation requiring assistance is detected (10). Subsequently, a data set for the situation is determined (11). This data set includes at least environmental data that was acquired before the situation occurred. Optionally, based on the data set, it is checked (12) whether an AI model implemented in the means of transportation is capable of resolving the situation. If this is not the case, the data set is sent to a control center (13). The control center receives (14) the data set and checks (15) whether an AI model of the control center is capable of resolving the situation. Instructions for resolving the situation are then generated (16), either by the appropriate AI model or by a human operator. The instructions are sent to the means of transportation (17).The means of transport receives (18) the instructions from the control center and executes them. Optionally, feedback can be sent to the control center (19) indicating whether the instructions were successful in resolving the situation. The invention further relates to a means of transport in which a method or device according to the invention is used, as well as a computer model for use in a method according to the invention.

Human operators (201A) perform activities within a facility in interactions with technical equipment (301A, 301B, 301C). A series (700) of images (701, 702) visualizes activities that are performed by either the same human operator (201A) or by different human operators. In the images, a computer separates areas (701x, 702x) that comprise biometric image data from areas (701y, 702y) with data that indicates the interactions. The computer obtains identifiers (ALPHA, ALPHA) of the operators (201A) but removes the biometric image. The computer then combines images areas and provides an audit record (500) by storing the modified first and second images (701′, 701′) and the identifiers.

This disclosure describes inspection tools and methods to inspect and maintain the interior of vent gas manifold channels. A magnetically-coupled probe, (e.g., a ball, cylinder, rectangular cube, or disc), is inserted inside the vent gas manifold channel. A magnetically-coupled head then magnetically drags the magnetically-coupled probe through the channels of the vent gas manifold. The inspection tool uses a sensor to monitor the position of the magnetically-coupled probe inside of the channel. If an obstruction is encountered, the sensor monitors local changes in one of more physical or electrical properties of the inspection tool and alerts an operator. The magnetically-coupled probe may be a steel bucket with sharp leading edges that detaches and scoops up the obstruction. A programmed robot manipulator arm or human operator may move the head across the surface of the vent gas manifold parallel to the channel.

Techniques for providing decision rationales for machine-learning guided processes are described herein. In some embodiments, the techniques described herein include processing queries for an explanation of an outcome of a set of one or more decisions guided by one or more machine-learning processes with supervision by at least one human operator. Responsive to receiving the query, a system determines, based on a set of one or more rationale data structures, whether the outcome was caused by human operator error or the one or more machine-learning processes. The system then generates a query response indicating whether the outcome was caused by the human operator error or the one or more machine-learning processes.

Systems and methods for controlling a performance of a digital character portrayed at a display device are disclosed. According to at least one embodiment, a method for controlling a performance of a digital character portrayed at a display device includes determining a presence of a person located in a physical environment, and in response to determining the presence of the person, facilitating control of the performance of the digital character portrayed at the display device by a human operator, by an artificial intelligence (AI) game engine, or by a combination thereof.

We provide a contact center system that can smoothly connect users to human operators if their requests cannot be resolved by automated responses such as chatbots. [Solution] In a contact center system equipped with a processor, when the processor receives a user request from a user terminal, it performs the following processes: extracting a standard response sentence corresponding to the request from a storage unit that stores standard response sentences and presenting it to the user terminal; and, if the request is not resolved by the standard response sentence, presenting an interactive element to the user terminal along with the standard response sentence for the user to start verbal communication with an operator. When the interactive element is operated by the user, it provides the user terminal with a connection to a pre-registered destination associated with the standard response sentence presented together with the interactive element.

A human operator (201A) performs an activity within a facility that interacts with the technical equipment (301A, 301B, 301C). A series (700) of images (701, 702) visualizes activities performed by the same human operator (201A) or different human operators. In the image, the computer separates a region (701x, 702x) including biometric image data from a region (701y, 702y) having data indicative of the interaction. The computer obtains an identifier (ALPHA, ALPHA) of the operator (201A) but removes the biometric image. The computer then combines the image regions and provides a review record (500) by storing the modified first and second images (701 ', 701') and the identifier.

The bi-directional man-machine interface may provide an electromotive force to move the dynamic component toward a predetermined alignment to provide visual feedback to a human operator, and the bi-directional man-machine interface may provide an electromotive force to provide positional stability and virtual stopper tactile feedback to a human user when manually positioned.

The present invention relates to systems and methods for ingesting of raw client data into a supply chain optimization system. The client enterprise data system (EDS) provides raw data, typically in tabular format across many files, to a data management module of the optimization system. This data is then profiled. and a set of mapping AI models is applied to the profiled data. Each of the plurality of mapping AI models corresponds to a single input feature of the optimization model. As the mapping models are applied, a live preview is generated. This live preview is reviewable by a human operator, and human input can be provided. When there is input, the individual mapping model is updated (without impacting the other mapping models), and a new live preview for that given feature is generated. The resulting standardized feature set may be consumed by the supply chain optimization model.

We provide the system. [Solution] Means for receiving inquiry data, A natural language processing means for analyzing the content of the aforementioned query data, A means for classifying the content of inquiries based on the aforementioned analysis results, A means of generating responses to inquiries using generative artificial intelligence, Means for transmitting the generated response, A means of transferring the inquiry to a human operator based on the aforementioned analysis results, A system that includes this.

A course of action (CoA) monitoring system comprises a sensor and a computing system. The sensor is configured to monitor tasks included in a course of action (CoA) performed by a human operator in an environment. The computing system is in signal communication with the sensor. The computing system includes a database that stores a plurality of reference CoAs defined by reference tasks having an intended target goal, and stores a trained task-based distributional semantic model configured to determine an intent similarity of the operator performing the tasks included in the CoA during real-time. The computing system inputs the monitored tasks determined by the sensor into the trained task-based distributional semantic model to determine a deviation between the reference tasks and the monitored tasks.

The present disclosure provides a control system for a humanoid robot comprising a bipedal action model (BAM) with hierarchical architecture including a beta model executing cognitive tasks at lower frequency, ingesting multimodal sensory inputs including visual data and natural language instructions, and an alpha model executing reactive tasks at higher frequency, communicatively coupled to the beta model. The BAM is trained on retargeted robot training data derived from robot-free training data. At runtime, the BAM outputs continuous control commands as parallel-generated action chunks controlling at least 18 degrees of freedom. The system includes a wearable collection apparatus capturing movement data from a human operator without physical connection to the robot, and a retargeting module translating robot-free training data into robot training data by solving embodiment mismatches between human and robot kinematic structures.

A system for robotic skill learning using vision-force servoing control. A robot performs an assembly operation and a camera, mounted in the workspace or on the robot arm, provides images of the operation. An image encoder neural network is trained to output a vision signal comprising pose features indicating distance from target location, where the vision signal is greatly reduced in size compared to the images. The vision signal is provided to a second neural network along with force / torque and tool center point velocity data. The second neural network operates as a vision-force servo controller, and is co-trained using offline reinforcement learning pre-training and online human operator correction. A pseudo-random sampling technique is used to improve the training of the vision-force control neural network. The vision-force servo controller outputs a next robot motion which is used by a compliance controller to control motion of the robot during the assembly operation.

Systems and methods presented herein facilitate operation of well-related tools. In certain embodiments, a variety of data (e.g., downhole data and / or surface data) may be collected to enable optimization of operations related to the well-related tools. In certain embodiments, the collected data may be provided as advisory data (e.g., presented to human operators of the well to inform control actions performed by the human operators) and / or used to facilitate automation of downhole processes and / or surface processes (e.g., which may be automatically performed by a computer implemented surface processing system (e.g., a well control system), without intervention from human operators). In certain embodiments, the systems and methods described herein may enhance downhole operations (e.g., milling operations) by improving the efficiency and utilization of data to enable performance optimization and improved resource controls of the downhole operations.

One embodiment is directed to a synthetic engagement system for process-based problem solving, comprising: a computing system comprising one or more operatively coupled computing resources; and a user interface operated by the computing system and configured to engage a human operator in accordance with a predetermined process configuration toward an established requirement based at least in part upon one or more specific facts; wherein the user interface is configured to allow the human operator to select and interactively engage one or more synthetic operators operated by the computing system to proceed through the predetermined process configuration, and to return result to the human operator selected to at least partially satisfy the established requirement; and wherein each of the one or more synthetic operators is informed by a convolutional neural network informed at least in part by historical actions of a particular actual human operator and a synthetic operator background configuration.

The invention relates to the technical field of robot control, in particular to a ground search and rescue robot sharing control method based on multi-level man-machine instruction fusion, which comprises the following steps: after a task-level instruction sent by a human is received, a robot performs global optimal path search; after a behavior-level instruction sent by a human is received, the robot performs risk assessment on the local environment in the map, and if a risk value is greater than or equal to a threshold value, the robot does not respond to the behavior-level instruction; otherwise, executing the behavior-level instruction, and searching the nearest path point in the global path to recover the task after execution. And after an action level instruction sent by a human operator is received, according to the calculated risk value, the man-machine authority is dynamically allocated, and an optimal control instruction is generated. According to the method, autonomous planning and manual intervention can be dynamically balanced, the adaptability to complex scenes is improved, the burden of operators is reduced, and the efficiency loss caused by high-frequency micro-operation is avoided.

The application relates to the technical field of autonomous value keeping based on a rule tree, and discloses an autonomous value keeping method and system based on a rule tree, which comprises the following steps: the autonomous value keeping method and system based on the rule tree provided by the application introduce a rule tree and a behavior logic engine, realize unmanned autonomous operation of a training station, break the dependence of traditional training on human operators, effectively reduce the idle time of the training station, and fully utilize existing resources; each operation task can be automatically executed according to a preset rule tree logic, the continuity and consistency of a training process are ensured, training interruption caused by the intermittence of manual operation is avoided, and the coherence of a training task is ensured; a dynamic rule reasoning mechanism is adopted, an operation decision path is adjusted through real-time state feedback, the judgment logic and the behavior mode of a real operator can be simulated, the system has a certain adaptability, and the intelligent level of simulation training is improved.

The invention relates to the technical field of mechanical arm control, in particular to a six-axis mechanical arm teleoperation and imitation learning system based on wireless communication and image transmission, which comprises a master-end six-axis mechanical arm, a master-end controller, a man-machine interaction screen, a slave-end six-axis mechanical arm, a slave-end controller and a task identification and switching module, the master end controller is provided with a master end wireless communication module and an AI imitation learning module. The slave end controller is provided with a slave end wireless communication module and an image transmission module. The master-end six-axis mechanical arm obtains operation input of a human operator and sends the operation input to the slave-end controller, and the slave-end controller drives the six-axis mechanical arm to execute corresponding actions; the AI imitation learning module is used for training a strategy network model based on the operation data of the two mechanical arms and learning a task control strategy so as to provide an autonomous control mode; and the task identification and switching module identifies the current task type, and switches between a manual teleoperation mode and an autonomous control mode in combination with a task control strategy.

The invention provides a telephone outbound man-machine cooperation judgment method and system based on AI. The method comprises the steps of obtaining multi-modal data such as texts, audio emotion and customer portraits; taking a text as a query, taking an emotion and a portrait as key values, and carrying out lightweight cross attention fusion and pooling to obtain fusion features; constructing a non-cyclic state pool, aggregating historical states through time decay, and splicing the historical states with the current features to form state enhanced representation; and inputting the representation into a lightweight scoring module, jointly predicting a turning probability and a dynamic threshold value, and judging whether turning to manpower or not according to a comparison result of the two. The method is low in time delay and high in judgment accuracy, continuous optimization can be carried out along with service feedback, and the service experience and the resource utilization rate are improved.

A twin roll casting system comprises counter-rotating casting rolls having a nip between the casting rolls and capable of delivering cast strip downwardly from the nip, a casting roll controller configured to adjust at least one process control setpoint between the casting rolls in response to control signals, a cast strip sensor capable of measuring at least one parameter of the cast strip, and a controller coupled to the cast strip sensor to receive cast strip measurement signals from the cast strip sensor and coupled to the casting roll controller to provide control signals to the casting roll controller, the controller comprising a reinforcement learning (RL) Agent. The RL Agent further comprises a model-free actor-critic agent having a value function and a policy function, the RL Agent having been trained on a plurality of casting system operation datasets composed of casting runs executed by a plurality of different human operators.

One embodiment is directed to a synthetic engagement system for process-based problem solving, comprising: a computing system comprising one or more operatively coupled computing resources; and a user interface operated by the computing system and configured to engage a human operator in accordance with a predetermined process configuration toward an established requirement based at least in part upon one or more specific facts; wherein the user interface is configured to allow the human operator to select and interactively engage one or more synthetic operators operated by the computing system to proceed through the predetermined process configuration, and to return result to the human operator selected to at least partially satisfy the established requirement; and wherein each of the one or more synthetic operators is informed by a convolutional neural network informed at least in part by historical actions of a particular actual human operator and a synthetic operator background configuration.