68 results about "Human operator" patented technology

The invention discloses a customer service robot system integrating knowledge base question and answer retrieval and work order processing, which comprises a natural language understanding and generating module, a knowledge retrieval module, a business system integration module, a dialogue management and decision module and a cue word management module, and relates to the technical field of artificial intelligence. Through cooperation of a large language model and a dialogue management module, the system realizes multiple rounds of deep understanding of a natural language, and can actively guide a user to clarify a demand, such as gradually inquiring an equipment model and fault details during repair, memorizing a dialogue context and avoiding information omission. Compared with traditional regular customer service, the method has the advantages that the accuracy of understanding complex problems is remarkably improved, open dialogue scenes such as technical consultation and fault diagnosis are supported, user interaction experience is closer to manual service seats, and the problems that multi-round dialogue understanding is limited, progress data cannot be obtained in real time, multiple knowledge bases are fused, and the automation degree of the business process is low are solved.

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.

The invention discloses a position-limited master-slave heterogeneous bilateral teleoperation control method based on an improved wave variable, a storage medium and a robot, and the method comprises the steps: building an improved wave variable algorithm framework, and adding a compensation wave to a slave input wave; a master robot task space reference trajectory and mapping of positions in a master-slave robot task space are constructed, and a master-slave robot joint space reference trajectory is solved based on a closed-loop inverse kinematics algorithm; based on a human operator, a far-end environment and master-slave robot characteristics, a combined robot dynamic model in a joint space is constructed; and aiming at the combined robot dynamic model, designing an adaptive neural network controller under limited joint positions based on a state transfer function. By means of the method, high-precision safety control over teleoperation of the master-slave heterogeneous robot with the limited position is effectively achieved, the stability and transparency of a teleoperation system are improved based on the designed improved wave variable algorithm, and a new safety operation method is provided for teleoperation control of the robot.

The invention belongs to the technical field of man-machine cooperative interaction, and relates to a man-machine cooperative compliant interaction method based on fuzzy variable admittance and human body motion prediction, which comprises the following steps: step 1, establishing a man-machine cooperative interaction admittance model, and converting the interaction force applied to an end effector of a mechanical arm by a collaborator into the end speed of the mechanical arm; 2, designing an admittance model parameter adaptive law based on a fuzzy theory, dynamically adjusting a virtual damping parameter, and designing a virtual mass adaptive law based on a virtual damping change condition, so that an admittance model dynamically adapts to a movement mode of a collaborator; and step 3, predicting a human body motion intention based on the LSTM network, generating an auxiliary speed item, and jointly generating an optimal man-machine compliant interaction speed in combination with an admittance reference speed obtained based on admittance model transformation. According to the method, the robot body motion information and the human body motion intention in a man-machine system are fully utilized, the self-adaptive admittance interaction device fused with the motion intention of the human operator is designed, and the naturalness and smoothness of man-machine interaction are improved.

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 invention provides a robot interactive local map construction method and system based on a depth camera, and relates to the technical field of robot real-time map construction. Comprising the following steps: acquiring a depth image and a color image through a depth camera, and acquiring IMU data through an IMU; the color image and IMU data are input into a global speedometer module, and the pose of the robot in a global coordinate system is estimated in real time; an elevation grid map with the robot as the center is constructed according to the global pose and the depth image, the topographic feature and the passing cost of each grid are calculated, and a local trafficability map is constructed; and obtaining a to-be-modified area selected by an operator in the color image, obtaining a map grid corresponding to the to-be-modified area in the color image based on the image alignment module, and modifying the passing cost of the corresponding grid to realize interactive modification of the local passability map. According to the method, the data of the depth camera and the operation intention of a human operator are combined, and interactive construction and modification of the map are realized.

Disclosed is a system for controlling artificial, robotic, or synthetic intelligence systems, the system comprising: a set of ethical and legal standards, a decision logic filter for referring output

The invention discloses a rope traction parallel robot reconstruction planning method for man-machine interaction, which belongs to the field of rope traction parallel robot configuration planning and comprises the following steps: step 1, setting parameters of a rope traction parallel robot and establishing a kinetic equation of a movable platform; 2, setting an admittance model between the mobile platform and the interactive person, and establishing a dynamic relationship between the mobile platform and the interactive person; 3, setting parameters and a kinetic equation according to the step 1, and establishing a mathematical model of a force feasible working space; step 4, dividing the man-machine interaction process into an interaction force stage and a non-interaction force stage according to the magnitude and duration of the force applied to the robot by the interaction person, and setting evaluation indexes of the two stages according to the mathematical model in the step 3; and step 5, according to the evaluation index and in combination with an admittance model, solving an optimal rope leading-out point position by using a reconstruction strategy, and completing reconstruction planning. According to the method, the rope leading-out point position can be solved in real time, and the working space size and the operation freedom in the man-machine interaction process are improved.

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.

A computer-implemented method of assessing driver capability for operating an autonomous vehicle includes identifying a human operator of the autonomous vehicle, the human operator being associated with a driving capability profile. The method further includes signaling to the human operator with the signal, the signal requesting a response from the human operator. The method also includes updating, based on the response from the human operator to the signal, the driving capability profile to indicate a level of skill of the human operator at responding to the signal.

Training software for human operators (1A), for example for a pilot student (5), to teach him or her to accomplish a training activity, for example a piloting training activity; the training software for human operators (1A) is storable in and executable by electronic processing resources (2) and designed to cause, when executed, said electronic processing resources (2) to become configured to cause the display (block 14), by means of electronic display or projection resources (3), of a rendered virtual working environment (7) corresponding to a real working environment in which the student (5) will have to operate; and to receive (block 14) from an electronic controller (4), operable by a student (5), a number of commands imparted by the student (5) and indicative of one or different actions to be performed by a virtual avatar (6) of the student (5) in the rendered virtual working environment (7). Furthermore, the training software for human operators (1A) is designed to cause, when executed, said electronic processing resources (2) to become configured to cause the display (block 14), by means of the electronic display or projection resources (3) and in response to, and on the basis of, the commands received from the electronic controller (4), of the execution of the actions associated with these commands and performed by the virtual avatar (6) of the student (5) in the rendered virtual working environment (7). Furthermore, the training software for human operators (1A) is designed to cause, when executed, the electronic processing resources (2) to become configured to implement a simulation model (8) configured to determine an action, and / or a sequence of actions, to be performed in order to accomplish a predefined training activity, in particular a piloting activity, based on a plurality (80) of actions and / or sequences of actions and based on evaluation scores (81) associated with them; wherein, the evaluation scores (81) are indicative of the contribution of such actions, or sequences of actions, (80) to the accomplishment of the predefined training activity in the rendered virtual working environment (7). The training software for human operators (1A) is further designed to cause, when executed, the electronic processing resources (2) to become configured to determine a number of actions to be performed, in order to accomplish a training activity to be learned, based on one or more actions performed by the virtual avatar (6) of the student (5) in the rendered virtual working environment (7), and on the basis of the simulation model (8) implemented. Furthermore, the training software for human operators (1A) is designed to cause, when executed, the electronic processing resources (22) to become configured to cause the display (block 14), by means of the electronic display or projection resources (3), of a teaching representation configured to guide the student (5) in learning the determined actions to be performed in order to accomplish the training activity to be learned in the rendered virtual working environment (7).

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.

The present invention relates to the field of model development technology, and discloses an intelligent agent orchestration method and system, the method comprising the following steps: extracting key content by receiving and processing information from project management tools and technical sources, and organizing it into a structured change request list; predicting the specific impact of each change request on the CI / CD process based on the structured change request list and existing configuration information; and outputting a set of reasonable configuration modification plans based on the prediction results. The present invention can automatically process each link in the CI / CD process, and can continuously optimize in a data-driven manner. At the same time, it can quickly respond to changes in demand and technology, minimize manual configuration changes, avoid introducing human errors, continuously optimize the CI / CD flow through data analysis and feedback loops, and use AI intelligent agents for self-adjustment and optimization to reduce dependence on human operators.

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.

The invention discloses a regulation and control algorithm for an unmanned clamping and holding vehicle based on human experience pre-training acceleration, which is characterized in that a machine learning model is driven by human experience data as a core, and decision logic and operation modes of a human operator in different operation scenes are simulated; the regulation and control algorithm of the unmanned clamping and holding vehicle can quickly obtain and master a certain degree of reasonable operation capability and strategy framework in the initial training stage. When a reinforcement learning training process is formally entered, the algorithm does not start to explore from zero, but performs targeted optimization and expansion on the basis of existing human experience, so that the convergence speed of the algorithm is greatly accelerated, unnecessary exploration and trial processes and wrong operations caused by blind trial and error are effectively reduced, and the accuracy of the algorithm is improved. And the overall performance and reliability of the unmanned clamping and embracing vehicle working in different scenes are remarkably improved.

A course of action (CoA) monitoring system (100) comprises a sensor (110) 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.

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.