182 results about "Behavior control" patented technology

A behavior control system according to an embodiment of the present invention recognizes the behavior of an artist and determines the behavior of an avatar corresponding to the recognized behavior of the artist. Then, the behavior control system controls the avatar on the basis of the determined behavior of the avatar.

A method and apparatus for controlling a behavior of a virtual character, and an electronic device and a storage medium, which relate to the technical field of games. The method comprises: acquiring a visual game image of a game environment at the current moment and a quantifiable game feature associated with a virtual character which is located in the game environment; using a target strategy behavior model obtained by means of pre-training, determining a target game behavior to be executed of the virtual character on the basis of the visual game image and the quantifiable game feature, wherein the target strategy behavior model is obtained by means of performing training on the basis of a large language model; and controlling the virtual character to execute the target game behavior in the game environment, such that the virtual character interacts with the game environment. By means of the present disclosure, the stability of the anthropomorphic effect can be significantly improved, and the overall anthropomorphic efficiency can be significantly improved.

The invention discloses an automatic driving generation model for reactive closed-loop evaluation, the automatic driving generation model comprises a behavior controller and a generative renderer, the behavior controller is based on a classic rule system IDM, and the generative renderer is based on a diffusion model; the workflow of the automatic driving generation model is as follows: S1, an end-to-end automatic driving model takes a current frame look-around picture as input, and outputs a planned future driving route of an own vehicle; on the framework, the simulation framework provided by the invention realizes high-frequency interaction with an end-to-end driving model by utilizing the generation capability of the generative model in an autoregression mode, and generates images consistent in time; the provided time consistency module, projection-based object level control and retrieval-based scene level control can significantly improve the model generation fidelity.

The invention relates to the technical field of virtual games, and discloses a virtual framework system of a game behavior control module, which comprises a data acquisition unit, an analysis processing unit and an instruction generation unit, and is characterized in that player operation and game environment data are acquired through an input equipment sensor, interference is removed through preprocessing, and reliable parameter analysis variables are acquired; in-depth player behavior mode analysis is carried out from multiple dimensions of operation frequency and coherence, and it can be accurately judged that players belong to aggressive, conservative or intermediate behavior modes. In game state evaluation, by means of indexes such as calculation distance and interaction frequency, the interaction state of a player and an interactive object can be accurately judged; on the basis, a game control instruction is generated, different instructions are adapted according to the player behavior mode and the game state, and the game experience is improved; the instruction is packaged in a preset format and is output through a TCP / IP protocol, so that stable and accurate transmission is ensured, and normal operation and experience coherence of a game system are ensured.

The invention relates to the field of robot control, in particular to an air-ground cooperative multi-task constraint following control method based on a Udwadiia-Kalaaba equation, and the method comprises the following steps: building a kinetic model of an air-ground cooperative system composed of unmanned control equipment, and building system constraints of the air-ground cooperative system according to a U-K method; based on the analysis of the control problem, establishing a multi-domain behavior control constraint between the unmanned control devices; the constraint following error is expressed as a control following object, and a control problem is converted into a solvable constraint following problem; multi-domain behavior control tasks among unmanned control devices are integrated into a problem framework, constraint force in the problem framework is obtained, and an adaptive robust controller is designed. By adopting the multi-task processing framework, a plurality of tasks can be integrated into a unified constraint, the complexity of the system is effectively reduced, and the conciseness and reliability are improved.

The invention discloses a method for intelligently controlling a digital human through an action tag, and relates to the technical field of intelligent control. The method comprises the following steps: S1, determining the range and purpose of actions, classifying the actions, and determining which specific application scenes and target user groups are to be used by an action library; s2, designing a technical label system used for describing various action features and providing a basis for technical action recognition and analysis; s3, establishing a behavior control model of the digital character according to the tagged action description; and S4, through cross validation, dividing the data set into a plurality of subsets for multiple times of training and verification, and adjusting the structure of the model according to an evaluation result. According to the method, the naturalness and fluency of digital character expression can be improved, the flexibility and customization capability of the technology are enhanced, and intelligent action selection and transition are realized, so that better user experience and technical support are provided for various application scenes.

The embodiment of the invention provides a game role behavior control method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring a plurality of time sequence paths generated based on interaction points in a game scene, wherein the interaction points are scene components for game roles to interact; determining the strategy value and access feasibility of each interaction point on each time sequence path; based on the strategy value of the interaction point, determining the overall strategy value of each time sequence path; determining a target time sequence path and an interaction point access time sequence on the target time sequence path based on the overall strategy value of the time sequence path and the access feasibility of each interaction point; and controlling the game role to access the time sequence to execute the target behavior based on the target time sequence path and the interaction point on the path. Through overall strategy value evaluation of a time sequence path, the problem of short view or stiffness of an AI decision in the prior art is solved, so that an AI role can select an optimal path based on long-term benefits; meanwhile, through dynamic calculation of access feasibility, decision failure caused by infeasible paths is avoided, and the real-time adaptability and simulation degree of AI behaviors are improved.

The invention relates to the field of home safety, in particular to an AI intelligent home safety control system. Comprising a management center, and the management center is in communication connection with a home user behavior module, a home user control module and a safety detection control module. Acquiring historical instruction data of a user for the smart home, analyzing the historical instruction data, constructing a user instruction map, and generating user control data in combination with the user instruction map; constructing a network path link in combination with the historical instruction data and the user control data; analyzing the network path chain to generate a standard path chain template; generating a real-time home path chain through the obtained current control behavior of the user, and obtaining a home behavior control model according to the user instruction map and the network path chain; and analyzing the collected real-time instruction data in combination with the home behavior control model, generating a risk mapping index table, and performing safety control on the smart home through the risk mapping index table. According to the invention, the security of the smart home can be improved.

The invention relates to a quadruped robot control method, which realizes efficient and stable control of a quadruped robot by combining a basic behavior controller (BBC), a task specific controller (TSC) and an evolutionary strategy simulator optimization method. The BBC learns diverse and natural behavior patterns through a semi-supervised information maximized generation adversarial imitation learning method; the TSC generates a control instruction through privileged learning and a self-supervised robust optimization method in combination with the depth image and proprietary perception information so as to realize efficient control of the quadruped robot; according to the evolutionary strategy simulator optimization method, by combining the ideas of the evolutionary strategy and the generative adversarial network, efficient optimization of simulator parameters is achieved, and the gap between the simulator and the real world is reduced. According to the method, the adaptability and task execution capability of the quadruped robot in a complex environment are remarkably improved.

The invention discloses a digital twinning-oriented animation linkage method and device, equipment and a storage medium. The method comprises the steps that based on classes in a domain ontology model and attributes of the classes, operation data of the Internet of Things system are converted into first semantic data, and the domain ontology model comprises an environment class, an equipment class and a behavior class; performing event identification on the first semantic data to obtain a first event; reasoning the first event based on a preset reasoning rule set to obtain a first reasoning result, the first reasoning result comprising a first response behavior for the first event; and controlling a digital twinborn body corresponding to the Internet of Things system to execute an animation matched with the first response behavior. Therefore, a closed-loop control chain of data driving, semantic recognition, event reasoning and animation linkage is formed, high reliability is achieved, complex dynamic linkage is supported, and therefore intelligent, automatic and visual expression of the system is effectively improved.

The embodiment of the invention provides a mechanical arm control system and method, and relates to the technical field of robots, the system comprises an execution process control subsystem, at least one mechanical arm behavior control subsystem and at least one visual behavior control subsystem, and the subsystems run in the same equipment. A mechanical arm and a visual device which are connected to the subsystems are deployed in the same working environment, and the execution process control subsystem is used for sequentially sending trigger instructions of control instructions to the target control subsystem according to the trigger sequence of the control instructions recorded by the execution process of a task; the mechanical arm behavior control subsystem is used for responding to the received triggering instruction and controlling the mechanical arm to execute the action indicated by the control instruction corresponding to the triggering instruction; and the visual behavior control subsystem is used for responding to the received trigger instruction and controlling the target visual equipment to execute the action indicated by the control instruction corresponding to the trigger instruction. Communication resources consumed in the mechanical arm control process can be reduced.

The invention belongs to the technical field of data security management, and provides an operation and maintenance audit management system and method, and the system comprises a behavior control module which is used for carrying out the compliance detection of an operation and maintenance operation after receiving an operation and maintenance request initiated by any operation and maintenance terminal, and blocking the execution of the operation and maintenance request when the compliance detection result is that the operation and maintenance operation is abnormal; the auditing module is used for acquiring key information corresponding to all the operation and maintenance operations, adding marking information to the abnormal operation and maintenance operations, receiving auditing suggestions added to the abnormal operation and maintenance operations by an auditing terminal, and generating an auditing report according to the key information, the marking information and the auditing suggestions; the management module is used for performing account management, authentication management, authorization management and workflow management on the operation and maintenance terminal; and the storage module is used for carrying out classified storage on the operation and maintenance whole-process data containing the compliance detection result, the key information, the mark information and the auditing opinions. According to the scheme, the accuracy, reliability and safety of the operation and maintenance auditing link are improved.

The application discloses a dynamic environment self-adaptive control method, device, equipment, medium and program product, and relates to the field of edge computing. The method comprises the following steps: determining a prediction result inference mode of the next behavior according to environment perception data in a shared cache and running state data of a to-be-controlled device. If the behavior prediction stops updating, a behavior prediction result stop updating identifier parameter is cached in the shared cache as the behavior prediction result. If the behavior prediction mode is adopted, different data elements of historical behavior data and behavior prediction data read from the shared cache are determined, and the final behavior prediction result is determined according to the behavior difference data and the historical behavior data. The application can solve the problems of poor real-time performance and large resource demand in related technologies, effectively reduce the response delay of behavior control of intelligent entity devices or virtual objects, and reduce the required computing resources and storage resources in the control process.

A set-top box with enhanced behavioral controls and system and method for use of the same are disclosed. In one embodiment of the set-top box, a wireless transceiver is located within a housing, which also interconnectively includes a signal input, a signal output, a processor, and memory. The set-top box may pair with an electronic door locking apparatus coupled to a door that is room-collocated with the set-top box in a hospitality establishment. The set-top box may then reprogram the electronic door locking apparatus as well as control amenities, such as a display, in response to presence signal from the electronic door locking apparatus.

The present invention discloses a method and system for humanoid impedance control in a robot based on multimodal signal fusion. The method comprises an imitation learning phase, a reinforcement learning phase, and a behavioral control phase. The imitation learning phase is used to learn task-related stiffness geometry from human demonstrations via sensors, transfer this geometry to the robot task using a motion redirection method, and then reproduce the learned trajectory profile and stiffness profile to train the robot's reinforcement learning model. The reinforcement learning phase is used to improve the controller's online adaptability, providing the task stiffness profile to the robot in an adaptively optimal manner, thereby performing humanoid impedance control on the robot's joint position, joint angle, and contact force. By setting an adaptive rate through the reinforcement learning model and establishing a compliance relationship between contact force and posture, the robot achieves compliant behavior in fine grasping tasks, addressing the need for adaptability to various objects during the robot's grasping force control process.

The invention relates to an interaction method and device of a virtual robot, a storage medium and electronic equipment. The method comprises the following steps: acquiring interaction information which is input by a user and interacts with the virtual robot; the interaction information is input into a control model of the virtual robot, and the control model is obtained through training by taking the interaction information input by a user of a video live broadcast platform and behavior response information of an anchor for the interaction information as model training samples; and according to behavior control information output by the control model based on the interaction information, performing behavior control on the virtual robot. According to the embodiment of the invention, interaction between the virtual robot and the user is realized, the real-time performance, the flexibility and the applicability of the virtual robot are improved, and the requirements of emotion and action communication between the user and the virtual robot are met.

The invention discloses a model optimization method and a related device, which can be applied to automatic driving, auxiliary driving, intelligent traffic, traffic simulation, vehicle-mounted scenes and the like. Perception data of the target object is obtained, an interpretation information prompt text is obtained, and the interpretation information prompt text is used for indicating the content understanding model to output the text content meeting the expectation. And outputting an object behavior control signal of the target object and decision explanation information matched with the explanation information prompt text through a content understanding model according to the perception data and the explanation information prompt text. If the action executed by the target object based on the object behavior control signal does not meet the preset target, that is, the object behavior control signal cannot control the target object to execute the correct action to cause avoidance failure, which link has a problem can be determined based on the decision interpretation information, so that the avoidance failure is avoided. Therefore, the content understanding model is adjusted more accurately and conveniently based on the decision interpretation information, and the performance of the content understanding model is improved.

The invention provides a non-player character control method and device, equipment, a storage medium and a program product. The method comprises the steps that non-player feature information of a non-player character in a virtual scene is acquired, and environment feature information of the environment where the non-player character is located is acquired; matching the non-player feature information and the environment feature information with trigger conditions associated with a plurality of candidate role behaviors, and determining a successfully matched target trigger condition; determining the candidate role behavior associated with the target triggering condition as a target role behavior to be executed by the non-player role; and controlling the non-player character to execute the target character behavior. By means of the method and device, the behavior control effect of the non-player character can be improved.

To allow for outputting appropriate information according to the emotions of a user.SOLUTION: An action control system disclosed herein is configured to detect occurrence of an event related to circumstances of a user, and output information to the user according to the detected event using an avatar controlled using a sentence generation model.SELECTED DRAWING: Figure 2

An autonomous mobile robot includes a drive system supporting the robot above a surface, a sensor system configured to generate a signal indicative of a position of the robot on the surface, and a controller operably connected to the drive system and the sensor system. The drive system is operable to navigate the robot around the surface. The controller is configured to execute the instructions to operate, including establishing a behavioral control zone on the surface, in response to establishing the behavioral control zone on the surface, controlling the drive system to manipulate the robot to a location of the behavioral control zone on the surface, and manipulating the robot around the surface using the drive system, and initiate a behavior in response to determining that the robot approaches the behavior control region based on the signal indicative of the position of the robot.

The application discloses a kind of based on behavior control's bionic robot action control method, it is related to technical bionic robot control field, including the following steps: establishing global terrain coordinate system, completing multi-sensor calibration, robot starts autonomous navigation task, vision sensor starts to collect surrounding environment image;Image is handled, and the color, texture feature in image is extracted;Interference object identification module identifies the interference object therein, and extracts its feature information;Interference index analysis module calculates interference index according to the feature information of interference object;Terrain environment complexity evaluation module real-time collection terrain environment information, evaluates the complexity of terrain environment;Action control decision module comprehensively interference index and terrain environment complexity evaluation result, makes action control decision, and sends control instruction to motion execution mechanism, the application solves the problem of bionic robot in complex environment motion intelligence and safety.

The behavior control system includes: a behavior recognition unit that recognizes a behavior of a user; a difficulty estimation unit that estimates the difficulty of a question when the behavior of the user is recognized as the question; and a behavior specifying unit that, when the score corresponding to the difficulty level is equal to or greater than a threshold value, specifies, as behaviors to be executed, a behavior that feels happy to the question asked by the user and a behavior that answers the question.

This invention relates to the field of distributed computing technology, and in particular to a unified registration and scheduling system for AI agent capabilities in edge-cloud collaboration, comprising: a dynamic behavior mapping module for capturing client interaction behavior features and mapping them to behavior control topology, performing path adjustment when deviations occur, and registering the control behavior to a unified MCP capability center; a semantic feature compression module for dimensionality reduction and compression of the original multimodal data stream to generate low-dimensional semantic feature stubs; an asynchronous state alignment module for converting feature stubs into asynchronous stateless message streams and reconstructing context mirror data on the cloud side; an inference stream scheduling module for generating inference stream scheduling strategies based on multi-dimensional states; a thought chain flow control module for dynamically truncating the streaming output of the large model according to the strategy and extracting core control segments and confidence levels; and a residual collaborative execution module for sending the core control segments to the edge for residual logic completion, driving the topology to execute control behaviors.

To allow for outputting information appropriate for an event that has occurred.SOLUTION: An action control system disclosed herein comprises a detection unit for detecting occurrence of a predetermined event, and an output control unit configured to provide control so as to cause an avatar controlled by a control device with a sentence generation model to output information to a user according to an event detected by the detection unit.SELECTED DRAWING: Figure 2

The application discloses a kind of unmanned passenger elevator car driving behavior control method, equipment and medium, it is related to passenger elevator car technical field, solve the problem that current unmanned passenger elevator car corresponding driving behavior exists security risk, driving behavior control method includes: step S1, set the driving rule of unmanned passenger elevator car, unmanned passenger elevator car executes driving operation according to driving rule;Step S2, the driving behavior data of unmanned passenger elevator car of execution driving operation is collected when collecting time node;Step S3, according to driving behavior data, the driving behavior of unmanned passenger elevator car is analyzed;Step S4, according to behavior control signal, the safety control of unmanned passenger elevator car is carried out, the application realizes the accurate analysis of unmanned passenger elevator car corresponding driving behavior, and realizes the safety control to driving behavior.

An integrated control method for a multi-spacecraft formation system based on predictive behavior control, comprising the following steps: Step S1: Define the global tasks to be executed by the multi-spacecraft formation: formation keeping, movement; local tasks: obstacle avoidance, and then establish a composite task through the way of behavior control projection in the null space; Step S2: Design a trajectory tracker based on decentralized model predictive control to track the reference trajectory; Step S3: Feed the predicted trajectory back to the planning layer for predicting the future task priorities, and extend the single-step planning in the traditional null space to multi-step prediction. Applying this technical solution can obtain a trajectory tracking control problem without task constraints, greatly reducing the online calculation cost. In addition, the predicted information on the future state of the spacecraft is considered in the process of determining the task priorities, making its switching effect more ideal compared with the traditional logic switching method.

The invention relates to an AGV control method and system, an intelligent terminal and a storage medium, and relates to the field of automatic processing, and the method comprises the steps: responding to a preset control condition, and transmitting a power supply control instruction to a wireless relay main station; a power supply control instruction is forwarded to a wireless relay slave station through a wireless relay master station, the power supply control instruction is used for indicating the wireless relay slave station to connect or disconnect a power supply loop of an AGV master controller supply point relay, and the AGV master controller supply point relay is used for connecting or disconnecting a power supply loop of a wireless client and an AGV master controller. The AGV main controller is used for controlling a power supply loop of the AGV equipment; and under the condition that the power supply control instruction belongs to a wake-up instruction, a behavior control instruction is sent to the wireless client through the wireless access point, the wireless client is used for forwarding the control instruction to the AGV main controller, and the AGV main controller is further used for instructing the AGV equipment to work according to the control instruction. The AGV control system has the effects of reducing energy consumption of the AGV control system and saving energy.

To suppress a sense of discomfort when performing vehicle behavior control using three actuators.SOLUTION: A vehicle behavior control apparatus includes: an i-th suspension disposed in a vehicular i-th wheel (i: 1 through 3); a fourth suspension disposed in a fourth wheel; and a controller. The i-th suspension includes an i-th actuator for providing the i-th wheel with vertical-direction brake force. The controller converts a required value of a behavior parameter indicating a vehicular behavior into i-th required brake force to the i-th actuator. The controller controls the i-th actuator on the basis of i-th required brake force, thus controlling a vehicular behavior. In the case where i-th required brake force (where k is any of 1 through 3) exceeds a k-th output range in accordance with output performance of the k-th actuator, the controller executes requirement reduction processing to reduce all of the first through third required brake force, and controls the i-th actuator on the basis of the i-th required brake force that is post requirement reduction processing.SELECTED DRAWING: Figure 3

The present invention discloses an automatic signal deployer, a signal deployment system, an automatic signal path deployment method, and a behavior control signal generation method of a deployment agent. The signal deployment system includes an automatic signal deployer, a deployment agent and a base station. The deployment agent receives signal quality data, generates a behavior control signal according to the signal quality data, and sends out the behavior control signal to the automatic signal deployer. The automatic signal deployer receives the behavior control signal and a source signal coming from the base station, performs deployment according to the behavior control signal, whereby the automatic signal deployer can transmit the source signal toward a signal path allocation direction and complete automatic deployment of signal paths.

The invention provides a virtual character behavior control method and device, electronic equipment and a storage medium, and relates to the technical field of games, and the method comprises the steps: obtaining a visual game image of a game environment at the current moment and quantifiable game features associated with a virtual character in the game environment; determining a target game behavior to be executed by the virtual character based on the visual game image and the quantifiable game features through a target strategy behavior model obtained through pre-training; wherein the target strategy behavior model is obtained based on large language model training; and controlling the virtual character to execute the target game behavior in the game environment, so that the virtual character interacts with the game environment. According to the method, the personification effect stability can be remarkably improved, and the overall personification efficiency is remarkably improved.