132 results about "Robot learning" patented technology

Systems and methods are provided for learning control policies that are safe, robust, and interpretable in developing robotic systems. In one example, a signal temporal logic is used to evaluate and rank the quality of demonstrations. The temporal logic based specifications allow generation of non-Markovian rewards, and also define causal dependencies between tasks such as sequential task specifications.

The invention discloses a visual servo double-arm robot migration simulation learning method from simulation to reality, which comprises the following steps: S110, constructing a virtual simulation scene according to a real task scene, and establishing a relation between the virtual simulation scene and the real task scene; s120, the collected teaching mechanical arm and executing mechanical arm operation data are replayed and optimized in the virtual simulation scene, so that a data set used for final training is constructed; s130, a deep imitation learning network is designed and achieved, input of the deep imitation learning network comprises visual data obtained in the real task scene and state data of all joint motors of the teaching mechanical arm, and output of the deep imitation learning network is predicted states of all joint motors of the execution mechanical arm at the next moment; and S140, migrating the fully trained and converged deep imitation learning network from a virtual simulation scene to a double-arm robot in a real task scene. According to the invention, the exploration efficiency and generalization ability of the two-arm robot learning network are improved.

The invention belongs to the technical field of robot learning, and particularly discloses a robot continuous imitation learning method and system based on a double diffusion model. Comprising the following steps: generating old task data in a submerged space by utilizing a pre-trained latent diffusion model, generating a model through guidance of a task identifier in combination with conditional diffusion, generating an image and state data related to an old task, modeling to construct a diffusion model, realizing multi-modal mapping from a state to an action, and generating a diversified action sequence; high-quality images and state data are generated through a pre-trained variational auto-encoder and a conditional diffusion model, and the consistency of the generated data in time and the accuracy of global semantics are ensured through time sequence continuity constraint and semantic consistency constraint; and performing feedback optimization training on the latent diffusion model. According to the method, the loss weight is dynamically adjusted, and the model is ensured to show good generalization ability and stability in a complex task in combination with collaborative training of an imitation learning strategy and a generator.

Systems and methods for performing document entity extraction are described herein. The method can include receiving an inference document and a target schema. The method can also include generating one or more document inputs from the inference document and one or more schema inputs from the target schema. The method can further include, for each combination of the document input and schema input, obtaining one or more extraction inputs by generating a respective extraction input based on the combination, providing the respective extraction input to the machine-learned model, and receiving a respective output of the machine-learned model based on the respective extraction. The method can also include validating the extracted entity data based on reference spatial locations and inference spatial locations and outputting the validated extracted entity data.

The invention provides a humanoid robot control method and system, a storage medium and a program product, and belongs to the field of computer vision. The method comprises the following steps: preprocessing expert action data, and processing the expert action data into expert data equivalent to a skeleton architecture of a target robot; in a simulation environment, a humanoid structure is adopted to build a robot, joint parameters of the robot are configured, and the degree of freedom of each joint is controlled by an independent physical control module; constructing a strategy representation method of the robot, wherein the strategy representation method comprises a state space, an action space, a reward function and a multi-frame control mode; the robot is initialized; and the difference between the robot action and the expert action on each frame is minimized, the reward function is maximized, and the robot is driven to learn. The method can assist the learning process of the humanoid robot, so that the robot can be personified while completing tasks, and the training speed is improved.

The technical scheme of the invention discloses a posture and size controllable high-degree-of-freedom soft hand device and a working method thereof, and the posture and size controllable high-degree-of-freedom soft hand device is characterized by comprising a mounting disc; the base, at least three finger assemblies, a first displacement mechanism, a connecting piece, a second displacement mechanism, a steering engine, a soft finger rotating mechanism, a soft finger and a limiting sleeve penetrate through the soft finger rotating mechanism, one end of the limiting sleeve is connected with the steering engine, and when the steering engine works, the soft finger rotating mechanism is controlled to rotate, and switching of working modes of the soft finger is achieved; and the background controller is electrically connected with and controls the first displacement mechanism, the second displacement mechanism, the steering engine and the pneumatic assembly to enable the three soft fingers to stretch, open, close, rotate and grab. According to the finger assembly, the posture and the size of the gripper can be automatically adjusted according to the shape and the size of a grabbed object, and the purposes of soft and stable clamping and operation of objects of multiple sizes, multiple shapes and multiple materials are achieved. The soft hand is low in manufacturing and using cost, convenient to assemble and suitable for the fields of soft robots and robot learning.

The invention relates to an artificial intelligence technology, can be applied to business system platforms of medical health, financial science and technology and the like, and discloses a robot control method, device and equipment based on artificial intelligence and a medium. Generating a planning path instruction of the robot according to the decomposition task; executing the path planning instruction in the simulation environment, and outputting operation track information and environment state change data of the robot; the simulation execution result is verified, and if verification succeeds, the natural language task instruction, the decomposition task, the operation track information and the success label serve as multi-modal information to be stored; training the initial diffusion strategy model based on the multi-modal information to generate a robot decision model; and controlling the robot based on the robot decision model. Through the automatic and large-scale data generation process, the robot learning efficiency and robustness are remarkably improved, and a natural language instruction can be responded to execute multiple tasks.

The invention relates to the technical field of robot control, in particular to a cross-modal perceptual driving body robot compliance control system which comprises the steps that a sensor is adopted to synchronously collect environment information, multi-source data bias is eliminated through a space-time alignment algorithm, a radial basis function neural network is adopted to analyze multi-modal fusion features, and a multi-modal model is obtained; human operation intention probability distribution is extracted to decompose a task into a path planning layer and a motion control layer, a collision-free trajectory is generated through an RRT algorithm, a high-fidelity physical engine is adopted to construct a virtual interaction scene, and robot learning results are shared through federal learning. According to the method, the problems of inaccurate perception, incoordination between intention recognition and interaction control, difficulty in control strategy verification, slow new task adaptation and difficulty in multi-robot learning result sharing caused by multi-source data deviation and large multi-modal semantic difference of the body robot in a complex environment are solved.

The invention discloses a robot training system and method based on plot memory in the technical field of artificial intelligence and robot learning, and solves the problems that an existing robot training method lacks an effective memory scheduling system, an empirical value quantification mechanism is not intelligent, and the multi-modal information fusion capability is insufficient. The system comprises a sensing module, a multi-modal unified memory encoder, a progressive memory scheduling system, a multi-dimensional value quantitative evaluation mechanism, an intelligent experience hierarchical scheduler, a multi-modal unified code retriever, a strategy generation module, an execution module and an anomaly detection module. The multi-modal unified memory encoder adopts a hierarchical dimensionality reduction Transform architecture, and fuses an RGB image, a depth image, force sensor data and joint angle information into a 576-dimensional unified feature vector; the progressive memory scheduling system comprises a working memory structure, a short-term memory structure and a long-term memory structure. The multi-dimensional value quantitative evaluation mechanism carries out quantitative scoring on experience based on reward evaluation, novelty evaluation and uncertainty evaluation.

The invention relates to the technical field of logistics robots, discloses a logistics robot learning system based on an artificial intelligence algorithm, and aims to solve the problem that a traditional logistics robot system lacks autonomous learning and environment change adaptation capabilities. The system integrates a path planning module, a task execution strategy generation module, a strategy adjustment module, a real-time monitoring module and a task configuration module, and can perceive a logistics environment in real time and plan an optimal path. And according to a path planning result, the system generates a specific task execution strategy, and continuously optimizes the strategy in a task execution process to cope with behavior differences. The real-time monitoring module ensures that the robot behavior is consistent with the planning result, and if deviation is found, strategy adjustment is triggered. The task configuration module configures targeted tasks according to robot performance and work requirements. According to the system, the autonomous learning and adaptive capacity of the logistics robot are improved, and the efficiency and safety of logistics operation are effectively improved.

The invention relates to the technical field of robot path planning, in particular to a navigation method and device of a mobile robot based on Transform-DQN, and can solve the problems that an existing algorithm is slow in algorithm convergence time, poor in path planning strategy performance and the like in a complex dynamic environment to a certain extent. According to the method, environment information and state information are obtained through multi-sensor fusion sensing environment; obtaining an expected action of the current mobile robot by using an optimal strategy obtained by a DQN algorithm; and controlling the movement of the mobile robot according to the current expected action. According to the technical scheme, the reward function considering multiple factors is set to interact with the mobile robot, so that the accuracy of the algorithm is improved; in the training process, an attenuation mode of an adjustable greedy factor is set, exploration and learning of the mobile robot in environments with different complexity degrees are balanced, a Transform model is introduced into an experience playback mechanism, the long-term dependency relationship between experiences is captured, the learning effect of the robot is enhanced, and the training efficiency is improved.

The invention discloses a multi-modal analysis method based on an intelligent robot, and relates to the technical field of robot learning, and the method comprises the steps: obtaining the sensing modal dimension of the intelligent robot; training single-mode decision models in one-to-one correspondence with the robot sensing modes; obtaining sensing data of the intelligent robot in each sensing mode; determining a single-mode decision scheme in each sensing mode; constructing a decision feature type set of each single-mode decision scheme and a decision feature value of each single-mode decision scheme; obtaining a total set of decision feature types; determining at least one alternative decision feature value of each decision feature in the decision feature type total set; performing cross fusion to obtain at least one preliminary screening decision feature group; screening out an optimal decision feature group of the intelligent robot; and outputting the multi-mode comprehensive decision of the intelligent robot. The method has the advantages that the optimal scheme under the multi-modal decision is effectively fused, and the multi-modal automatic optimal decision of the intelligent robot is realized.

The invention discloses a robot imitation learning method based on a diffusion model, and belongs to the technical field of robot learning and body intelligence. Comprising the following steps: carrying out standardization processing on input image observation data and a robot state, and carrying out image enhancement by adopting gray scale transformation, random erasure and Gaussian blur; based on the enhanced image and the robot state, training a diffusion model to generate an action sequence, including forward diffusion, conditional feature fusion, noise prediction and joint loss optimization; in real-time control, features are extracted through an image enhancement network, sampling is accelerated through DDIM to generate an action sequence, noise scheduling coefficients are dynamically adjusted based on visual feature differences, and closed-loop optimization is achieved. According to the method, the image enhancement technology and the diffusion strategy are deeply fused, joint optimization of visual features and action sequences is achieved, the action generation accuracy and robustness of the robot under the complex visual interference and small sample conditions are remarkably improved, and the success rate is improved by 18% compared with a base line under 40 sample sizes.

According to the humanoid robot tumble recovery control method based on multi-stage curriculum learning, robot tumble crawling actions are decomposed into a plurality of key frames, frame-by-frame staged learning is carried out, meanwhile, a mixed internal model and a reinforcement learning framework are introduced, the mixed internal model obtains a current observation value of a robot in real time, and through speed estimation, the robot tumble recovery control method based on multi-stage curriculum learning is realized. According to the method, feature information is obtained, the robustness of a strategy is enhanced, the difference between simulation and reality is reduced, a reinforced semester framework mixing internal optimization and near-end strategy optimization is combined, and through interaction with a simulation environment, robot learning is rapidly and stably transited from one key frame to the next key frame, learning of complex human shape motion behaviors is achieved, and the learning efficiency is improved. According to the application, the man-machine interaction safety performance is optimized, so that the robot can reliably operate in scenes needing close man-machine cooperation, such as medical care and home service, and a technical foundation is laid for constructing a more intelligent and safer next-generation service robot system.

A method may include receiving input data identifying an operation to be completed in an environment. The method may also include identifying, using an artificial intelligence (AI) model, a series of tasks to be performed by robots to complete the operation based on the input data. In addition, the method may include identifying a subset of the robots to perform the series of tasks based on capabilities to be used to perform the series of tasks. Further, the method may include causing the subset of the robots to autonomously perform the series of tasks to complete the operation.

A method for improving a mobile robot configured to perform a task in an environment using an operational program is disclosed. Data is received, the data being recorded by the mobile robot using one or more sensors when the mobile robot is navigated in the environment to perform the task. A database and / or model associated with the environment is updated to include the recorded data therein. The operational procedure of the mobile robot can be modified based on the database and / or the model to generate a modified operational procedure for performing the task in the environment, the modified operational procedure improving performance of the mobile robot. Furthermore, based on the database and / or the model, suggestions for improving the performance of the mobile robot in performing the task in the environment can be determined and displayed to a user for consideration.

A robotic training system enabling intuitive skill transfer through human demonstration using paired devices, allowing robots to perform complex manipulation tasks traditionally requiring extensive manual programming or expensive hardware setups. Leader robotic devices configured for human manipulation and follower robotic devices replicate the leader's movements. Force sensors, torque sensors, or force-torque sensors measure and process force data, torque data, or force-torque data for recording training demonstrations for artificial intelligence (AI) models. Demonstration devices include user control interfaces, multiple workspace viewpoints, motion modification for haptic feedback, and interchangeable end effector tools. A control system enables automatic transitions between position and force control based on sensed interactions, while incorporating learned policies from demonstrations with visual and force data. Methods for robot programming leverage demonstration data, generating natural language descriptions of actions and human-readable narratives of robot programs. The system provides a comprehensive, low-cost solution for robot learning from human demonstration.

The invention provides a robot learning data set construction method and related equipment, and relates to the technical field of humanoid robots. According to the method, a robot operator remotely operates the humanoid robot to execute various operation tasks based on inertial motion capture equipment, and real-time operation images and real-time joint motion data of the humanoid robot in the process of executing the operation tasks under manual guidance are collected to construct a data set; through organic combination of a human body posture direct extraction characteristic of inertial motion capture equipment and task execution flexibility and task execution reliability of robot teleoperation, construction efficiency, data set validity and data set integrity of a task learning data set are improved; and generalization and accuracy of simulation learning of the humanoid robot can be further improved.

The invention discloses a digital intelligent engineer project practical training teaching method based on robot learning, and relates to the technical field of teaching. Task difficulty and content are adjusted in real time according to personal performance and ability states of students through dynamic task allocation and personalized learning path optimization; the self-adaptive teaching mode enables each student to learn at proper rhythm and difficulty, a project management tool, a version control system and a real-time feedback mechanism are integrated, the students can obtain real-time guidance and optimization suggestions in actual operation, real-time operation suggestions are provided during code submission and version merging, and the teaching efficiency is improved. And the version snapshot is automatically generated to be associated with the task progress, so that students continuously optimize own operations in practice, automatically generate learning reports, evaluate code quality, task completion efficiency and version control rationality of the students, provide comprehensive learning feedback for the students, and provide data support for teachers to improve teaching strategies.

The present invention provides a humanoid robot control method, system, storage medium and program product, belonging to the field of computer vision. The method includes: preprocessing expert action data and processing the expert action data into expert data equivalent to the skeletal structure of the target robot; building a robot with a humanoid structure in a simulation environment, configuring the joint parameters of the robot, and each joint degree of freedom is controlled by an independent physical control module; constructing a policy representation method for the robot, including a state space, an action space, a reward function, and a multi-frame control method; initializing the robot; minimizing the difference between the robot action and the expert action in each frame, maximizing the reward function, and driving the robot to learn. The present invention can assist the learning process of the humanoid robot, enabling the robot to be anthropomorphic while completing tasks and improving the training speed.

The present disclosure provides a control method, device, medium and speech processing-based debate robot system for a speech processing-based debate robot. The control method for the speech processing-based debate robot includes: obtaining a debate material training set; inputting the debate material training set into a preset training model to control the debate robot to learn the debate materials, where the preset training model includes a low-rank adapter for optimizing the preset training model; inputting a preset debate scenario, the debate duration corresponding to the preset debate scenario, and the debater role of the debate robot into the debate robot; and controlling the debate robot to output target debate language within the debate duration corresponding to the preset debate scenario according to the preset debate scenario and the speech information of the debate scenario. Through the present disclosure, a robot debater can be generated to adapt to various debate scenarios and break through time limitations to accompany humans in debate training or debate competitions.

The application provides a method for determining development parameters of a natural gas hydrate reservoir and related equipment. In the process of particle swarm intelligent optimization, a robot learning algorithm is used for data mining and training to obtain a training model. The potential particles are determined based on the training model. The global optimal objective function is determined based on the combination of the potential particles and the updated development parameters. In the case where the convergence condition is reached based on the development parameter combination corresponding to the global optimal objective function value, the development parameter combination corresponding to the global optimal objective function value is determined as the target development parameter combination. The calculation process of the intelligent optimization algorithm can be improved by the machine learning algorithm, and the optimization time of the development parameters of the natural gas hydrate reservoir is shortened.

Robot teaching device (30) that teaches a robot, wherein the robot teaching device (30) an image acquisition unit (34) that acquires distance images representing distance information of a subject to be photographed or two-dimensional images of the subject to be photographed as moving images; a trajectory detection unit (311) that detects the movement trajectory of an operator's hand captured as a recording object in the moving images using the moving images; a detection unit (312) which detects whether the detected trajectory represents a straight-line trajectory or a rotational trajectory; and a command generation unit (313) that generates a command that makes a specific movable portion of the robot perform a translational movement based on the movement trajectory when it is detected that the movement trajectory is a rectilinear movement, and makes the specific movable portion of the robot perform a rotational movement based on the movement trajectory when it is detected that the movement trajectory is a rotational movement.

A data collection system that performs data collection of human-driven robot actions for robot learning. The data collection system includes: i) a wearable computation subsystem that is worn by a human data collector and that controls the data collection process and ii) a human-machine operation interface subsystem that allows the human data collector to use the human-machine operation interface to operate an attached robotic gripper to perform one or more actions. A user interface subsystem receives instructions from the wearable computation subsystem that direct the human data collector to perform the one or more actions using the human-machine operation interface subsystem. A visual sensing subsystem includes one or more cameras that collect raw visual data related to the pose and movement of the robotic gripper while performing the one or more actions. A data collection subsystem receives collected data related to the one or more actions.

Systems and methods for performing document entity extraction are described herein. The method can include receiving an inference document and a target schema. The method can also include generating one or more document inputs from the inference document and one or more schema inputs from the target schema. The method can further include, for each combination of the document input and schema input, obtaining one or more extraction inputs by generating a respective extraction input based on the combination, providing the respective extraction input to the machine-learned model, and receiving a respective output of the machine-learned model based on the respective extraction. The method can also include validating the extracted entity data based on reference spatial locations and inference spatial locations and outputting the validated extracted entity data.

The application discloses a kind of robot learning methods, equipment and medium based on blockchain, method includes: determining the blockchain platform created based on blockchain framework in advance;Determine operation instruction, and write operation instruction in blockchain platform;Receive the operation request sent by robot;According to operation request, and the operation instruction stored in blockchain platform, determine the current operation instruction corresponding to the robot;Send current operation instruction to robot, to make robot execute corresponding operation mode based on current operation instruction.The whole process of robot learning and obtaining operation instruction is stored in blockchain platform.As blockchain platform is distributed storage, data tampering of single node will not take effect, which ensures the authenticity and credibility of data on the blockchain platform.When robot encounters new working scenario, it only needs to obtain corresponding operation instruction in blockchain platform to work, without artificial re-editing, which improves work efficiency.

The present application relates to a kind of soft landing reward function-based quadruped robot low-noise gait control method and system, the method includes: constructing quadruped robot motion control environment, and defining foot contact determination standard;Design soft landing reward function based on vertical contact force, for the negative punishment of large contact force at the moment of landing, guide quadruped robot to learn low contact force landing mode;Collect the real-time contact force of quadruped robot foot and historical contact state, with soft landing reward function as core optimization target, using reinforcement learning algorithm, in combination with gait stability constraint, iterative training is carried out to strategy network, and low-noise gait strategy network is obtained, for optimizing output gait control signal, to control the gait action of quadruped robot accordingly.Compared with prior art, the present application can consider the noise control and gait stability of robot by accurately determining the moment of landing, designing contact force negative reward and combining reinforcement learning to actively optimize gait strategy.

The present invention discloses a multi-robot assembly line method and system based on a temporal logic control strategy, comprising: expressing the robot's task specification based on a control strategy synthesized by temporal logic of a parity check game, constructing a reward automaton with a potential energy function according to the acceptance condition of the synthesized strategy to assign a reward value to the robot's behavior; decomposing the comprehensive strategy of a robot group consisting of a generalized reactive specification of rank 1 into a reward automaton for each robot, and expanding the reward automaton with potential energy on the MDP; proposing a reward shaping algorithm based on value iteration and a distributed Q-learning algorithm to improve the speed at which the robot group learns the optimal strategy; the present invention captures the temporal attributes of the task based on temporal logic, decomposes the generated comprehensive strategy into multiple individual reward automata to guide the robots to learn the optimal strategy, and proposes a reward shaping algorithm based on value iteration to improve the efficiency of the robot group in learning the optimal assembly strategy and avoid falling into the problem of falling into local optimality.