1019 results about "Robot motion" patented technology

The invention discloses a multi-welding-robot collaborative operation system based on digital twinning, and belongs to the technical field of intelligent manufacturing and robot control, the system comprises a physical space module, a virtual space module, a twinning data layer and a control module, and communication connection is established among the physical space module, the virtual space module, the twinning data layer and the control module; the physical space module comprises a plurality of welding robots, a sensor group and a data transmission network; the virtual space module comprises a multi-welding robot twinning body and a workpiece twinning model; the twin data layer is used for connecting the physical space module and the virtual space module; and the control module generates a welding path optimization scheme, a multi-welding-robot collaborative collision avoidance strategy and a welding quality feedback control instruction based on the twin data layer. According to the method, a closed-loop control framework of'physical-virtual 'bidirectional mapping is constructed, so that dynamic collaborative optimization of multi-robot motion trails, welding seam forming and thermal deformation is realized.

The invention relates to the technical field of robot control, and discloses a heavy-load robot motion trail method and system based on machine learning. The method comprises the steps that historical movement track data of the heavy-load robot in a working scene are collected, and the data comprise a joint position sequence, an end effector pose sequence and environment obstacle distribution information; the data is preprocessed, track features are extracted, a space-time correlation matrix is constructed, and the matrix is used for representing the dynamic coupling relation between joint movement and the tail end pose; training a trajectory prediction model containing a long and short-term memory network and an attention mechanism based on the matrix, and generating a collaborative mapping relation between a joint position and a tail end pose; obtaining a current task target pose sequence and an environment constraint condition in real time, and outputting a candidate track set meeting dynamic constraint through a model; and adopting a multi-objective optimization algorithm to screen candidate tracks, generating an optimal track instruction and issuing the optimal track instruction to an execution mechanism. The method adapts to the complex characteristics and variable working conditions of the heavy-load robot, and the track adaptability is improved.

The invention discloses a quadruped robot obstacle avoidance control method and system based on path planning, and relates to the technical field of quadruped robot obstacle avoidance control, and the method comprises the steps: obtaining environment point cloud data, image data and quadruped robot motion speed data, and carrying out the terrain semantic segmentation and semantic pixel back projection of the image data, thereby obtaining a quadruped robot obstacle avoidance result; generating semantic point cloud data, combining the semantic point cloud data with the environment point cloud data subjected to motion compensation, and constructing a semantic annotation grid map; performing global path search on the semantic annotation grid map to generate a global smooth path; when the quadruped robot advances along the global smooth path, path nodes on the global smooth path are extracted at a fixed step pitch, the terrain category and the grid occupation state of each path node are judged according to the semantic annotation grid map, and a local reference path is generated; and executing a multi-stage obstacle avoidance strategy on the local reference path, and generating a foot end track sequence.

The invention relates to the technical field of photovoltaic equipment cleaning, and discloses an autonomous path planning system and method based on a photovoltaic cleaning robot. The system comprises an environment sensing module which is used for collecting three-dimensional point cloud data on the surface of a photovoltaic panel and reducing noise; the path planning module is used for constructing a topological map and optimizing a global path; the obstacle detection module is used for identifying obstacles and extracting physical attributes of the obstacles; the dynamic adjustment module is used for analyzing path conflicts and dynamically re-planning; and the execution control module generates a robot motion instruction. According to the method, through cooperative work of the modules, efficient path planning and cleaning work of the photovoltaic cleaning robot in a complex environment are achieved. Compared with a traditional cleaning mode, the cleaning efficiency and quality are improved, the cost is reduced, the adaptability of equipment to a complex environment is enhanced, and powerful technical support is provided for development of the photovoltaic industry.

The invention discloses a rescue method and system of a rescue robot for exploration, and relates to the technical field of underground space rescue, and the method comprises the following steps: obtaining multi-path acoustic echo data of a karst cave and motion track data of the robot, and constructing a three-dimensional point cloud model according to the multi-path acoustic echo data and the motion track data of the robot; and identifying unmatched data in the multi-path acoustic echo data and the robot motion trail data, and taking an area where the unmatched data is located as an abnormal area. According to the method, the spatial resolution of signal acquisition is enhanced through the multi-microphone array, robust acoustic fingerprints are extracted in combination with a noise reduction algorithm and short-time Fourier transform, high-confidence human body sound source signals are screened out by using a feature template matching mechanism, accurate extraction and recognition of human body acoustic features in a karst cave complex noise environment are realized, and the accuracy of human body acoustic feature recognition is improved. The problem of misjudgment caused by confusion of sound source features and environmental noise in a traditional method is effectively solved.

The invention discloses a humanoid robot somatosensory control system and method based on deep learning. The method comprises the steps that physiological data and environment information of a user are collected through a wearable device and an environment sensor, and full-modal synchronous collection is achieved in combination with audio and visual data. The system carries out noise reduction processing on the collected sound, extracts a voice instruction and recognizes non-language sound features to judge the physiological state of the user; and analyzing the posture and gesture intention of the user through a human body detection and posture recognition algorithm. And multi-modal data is further fused, the intention of a user is accurately judged in combination with scene context and a security verification loop, a multi-dimensional state space and an atomic action library are constructed, and a composite reward function is designed to realize intelligent decision and execution of robot actions. The system comprises a multi-modal data acquisition module, a user intention understanding module, a dynamic action planning module, a safety monitoring and exception handling module and a continuous learning and collaborative optimization module, and can effectively improve the intention recognition precision, environmental adaptability and task execution efficiency of the robot in a complex home scene. And intelligent and personalized family service support is provided for the elderly.

The invention belongs to the technical field of robot welding, and discloses a welding robot motion path optimization planning method based on multi-sensor fusion. Comprising the steps that sensor data are obtained, and the sensor data comprise a welding seam three-dimensional point cloud, a molten pool image, acceleration and angular velocity of the tail end of a welding gun in three axial directions, welding current and welding voltage; converting the sensor data into a unified coordinate system to obtain converted sensor data; performing filtering processing on the converted three-dimensional point cloud of the welding seam to obtain a filtering point cloud, and performing feature extraction on the filtering point cloud based on an RANSAC (Random Sample Consensus) algorithm to obtain a center line feature of the welding seam; in combination with the converted three-dimensional point cloud of the welding seam and the characteristics of the center line of the welding seam, an initial welding path is obtained through planning based on an improved A * algorithm; intelligent and precise adjustment of welding parameters is achieved, the stability of welding quality is guaranteed, and the performance of motion path planning and welding operation of the welding robot is improved in an all-around mode.

The invention discloses a robot obstacle avoidance method and system based on millimeter wave radar sparse point cloud, and relates to the technical field of obstacle avoidance recognition. A robot obstacle avoidance system based on millimeter wave radar sparse point cloud comprises a point cloud acquisition module, a negative obstacle identification module, a weak obstacle identification module, a point cluster identification module, a risk map module, a tentative verification module and an obstacle avoidance decision module. According to the invention, suspected obstacle point clusters are extracted based on a reflection intensity threshold and a spatial proximity relation in an enhanced point cloud, a theoretical parallax model of a real static obstacle is constructed under the constraint of a robot motion trajectory, and Doppler velocity distribution of each frame is combined with a static obstacle Doppler physical law for comparison. And classifying the point clusters which do not meet the multi-view geometric consistency or Doppler physical law, and distinguishing multipath false point clusters from dynamic point clusters.

The invention discloses a joint denoising method for robot visual motion prediction, and the method comprises the steps: constructing a unified generative model through fusing an image and a depth map collected by a depth camera, motion data collected by CAN line communication of a Piper mechanical arm, and a tactile image collected by a Gelsight Mini tactile sensor; the method comprises two steps of data acquisition and input coding, and joint denoising and generation: firstly, multi-modal data are coded into low-dimensional potential representation, and then future images, depth maps, tactile data and robot actions are cooperatively predicted through a joint denoising framework based on Transform. A mask self-attention mechanism is innovatively introduced, information interaction between modes is dynamically adjusted, action generation is guided through tactile feedback, and the force control precision is improved. The model adopts a de-noising diffusion probability loss function to jointly optimize multi-modal prediction, so that the output consistency is ensured. According to the method, the robustness and the accuracy of flexible operation of the robot are remarkably improved.

The invention belongs to the technical field of robot operation and control, and discloses a robot action generation method and a related device. The robot action generation method comprises the steps of obtaining a task instruction, ontology perception of a to-be-operated robot at the current moment, a multi-view RGB-D image and an initialized current noise action; and utilizing the trained action consistency diffusion strategy model to perform iterative action denoising according to the obtained task instruction, the ontology perception of the to-be-operated robot at the current moment, the multi-view RGB-D image and the initialized current noise action, and generating the action of the next step of the to-be-operated robot. According to the technical scheme disclosed by the invention, the technical problems of high training modeling difficulty, insufficient action prediction precision, low action generation speed and the like of the existing robot action generation technology in a high-precision operation task of the robot can be solved.

The invention belongs to the field of robot control, and particularly relates to a robot self-adaptive impeller welding control method and system fused with multi-modal data, and the method comprises the steps: obtaining an impeller workpiece image and three-dimensional point cloud data through an industrial vision subsystem, and extracting a three-dimensional welding seam position point set representing a welding seam track; generating a candidate welding path set based on welding seam geometric feature analysis; in combination with welding seam morphology and process requirements, optimal welding process parameters are predicted through a regression model, and a welding gun attitude angle is calculated through quaternion conversion according to a curved surface normal vector; in the welding process, the relative position deviation of a robot and a workpiece and the parameter deviation of welding current / voltage and a planned value are monitored in real time, when the deviation exceeds a preset tolerance interval, a PID control algorithm is adopted to dynamically adjust robot motion parameters or welding process parameters, and closed-loop control over the welding process is achieved; according to the method, precise planning of the welding path and self-adaptive regulation and control of the process are achieved, and the welding quality and the forming consistency are remarkably improved.

The invention discloses a ship plane segmented welding multi-robot task distribution system based on digital twinning, and relates to the technical field of ship intelligent manufacturing, and the system comprises a four-dimensional digital twinning body module which is used for collecting workpiece assembly errors, welding seam positions and robot state data in real time; and constructing a twin model comprising a physical engine layer, a process knowledge base and a dynamic topological graph. According to the ship plane segmented welding multi-robot task allocation system based on digital twinning, workpiece assembly errors, robot motion states and welding process rules are fused in real time through the digital twinning body, a weld joint task topological graph is dynamically constructed, and multi-robot task accurate allocation is achieved in combination with an anti-conflict auction algorithm. By means of a space-time gridding cooperation mechanism, motion conflicts between a large gantry mechanism and a small gantry mechanism are effectively solved, and zero interference of a welding path is guaranteed; the local path optimization module adaptively adjusts the posture and the dry extension length of a welding gun for a narrow space, and eliminates the physical collision risk.

The invention provides a robot control method and device, equipment and a storage medium, and relates to the technical field of robot control. The method comprises the steps of obtaining robot data of a robot executing a task, and determining a learning data set according to the data; building a first robot action prediction model based on the initial visual language model; obtaining a second robot action prediction model according to the data set and the first robot action prediction model; a robot time sequence, image data and a language instruction are taken and input into a second robot action prediction model, the time sequence data are input into a second language model through a preset time sequence model and a multi-layer projector, the image data are input into the second language model through a visual encoder and the projector, and the language instruction is input into the second language model through the first language model; the second language model outputs continuous actions to be executed; and completing the task according to continuous action control. And the time sequence model captures time sequence characteristics, so that the second robot action prediction model outputs continuous actions to be executed, and the operation smoothness and continuity are effectively guaranteed.

The invention belongs to the technical field of aircraft coating processing, and particularly relates to an aircraft surface automatic spraying method based on a heavy-load robot, which comprises the following steps: carrying out geometric modeling on an aircraft body workpiece; a spray gun tool coordinate system is calibrated, and TCP coordinate optimization calculation is carried out by fusing spray gun 3D model parameters and flame long distance values; constructing a global workpiece coordinate system dynamic database; automatic coverage of a multi-layer U-shaped reciprocating spraying path is achieved through a path planning algorithm; after a fixed spraying distance parameter is set, the spraying software process package generates a robot track, and path simulation is carried out; a robot movement track program is generated by combining parameters of coating types, plasma flame lengths, spraying angles and spraying speeds; and a low-speed transition point is inserted into a track alarm point, and dynamic smooth connection of a spraying path is achieved. The spraying efficiency of the large machine body workpiece is improved, and manual intervention is reduced; and the adaptability is high, and spraying objects of different shapes and sizes can be rapidly dealt with.

The embodiment of the invention provides a robot control method and related equipment. The robot control method specifically comprises the steps that target action information used for controlling robot movement is generated based on a robot control task; performing security verification on the target action information based on the motion security constraint information of the robot; wherein the motion safety constraint information is used for constraining the motion range of the robot in the space from at least one spatial dimension; under the condition that the security verification is not passed, optimizing the target action information until the optimized target action information passes the security verification; and driving the robot to move by using the target action information passing the safety verification. According to the invention, unsafe robot behaviors can be reduced.

The invention discloses a bone grinding reinforcement learning system and method based on near-end strategy optimization, and the method comprises the steps: learning a relation between a tail end cutting force state and a robot motion in a human trial and error growth mode, and controlling a Markov decision process problem through a robot force; contact force control is optimized through a near-end strategy; designing a reward function and a penalty function of the bone grinding action; performing accelerated training on a robot action decision strategy; and factors influencing the bone cutting effect of the robot are analyzed. On the basis that the robot bone resection control process has the Markov property, the PPO algorithm is adopted to train a controller of the grinding robot, and the robot is guided to obtain a positive value reward; parallelization calculation and closing of graphic display are helpful for increasing the training speed; by coordinating a reinforcement learning strategy of the robot and combining and utilizing priori knowledge of human or a PID control algorithm for guidance, invalid exploration behaviors are avoided, and an efficient learning process is realized.

Techniques for robot control using multi-modal user inputs include receiving one or more multi-modal inputs from a user, extracting a motion hint from the one or more multi-modal inputs, generating estimated noise based on a current motion scene for the robot, generating a plurality of candidate motion plans, iteratively denoising the plurality of candidate motion plans based on the estimated noise and the motion hint to generate a plurality of revised robot motion plans, selecting a robot motion plan from the plurality of revised robot motion plans, generating a robot trajectory from the selected robot motion plan; and commanding the robot to perform a first step of the robot trajectory.

This patent discloses a computer system for humanoid robot control system design and implementation, featuring a digital humanoid robot with dynamical models and a set of single-input-single-output (SISO) and multi-input-multi-output (MIMO) controllers. The system comprises a main software program, a generative Al humanoid robot intelligence engine, a robot motion path planner module, and a control system simulation engine. It enables efficient design, testing, validation, and implementation of robot control systems, significantly reducing time to market. The system supports seamless upgrades to accommodate new designs and components, enhancing applications in industrial automation, healthcare, public safety, and more, aligning with the goals of the 4th Industrial Revolution.

The invention provides a robot production line article grabbing method based on visual localization, which comprises the following steps: preprocessing a multi-modal image to obtain an original image; performing grid mapping on the original image to obtain a multi-level feature descriptor; based on the multi-level feature descriptors, a dynamic mapping relation among the three coordinate systems is established through a non-rigid coordinate system alignment algorithm, and coordinate offset of movement of the conveyor belt is compensated in real time; performing hierarchical feature matching on the multi-level feature descriptors and an article template library, identifying article categories and extracting contour geometric features; based on the contour geometric features and the surface curvature distribution, the three-dimensional pose and candidate grabbing points of the object are obtained through a geometric constraint optimization algorithm; and generating a robot obstacle avoidance track according to the candidate grabbing points and the robot motion model. According to the method, accurate coordinate compensation is realized through multi-modal data fusion and dynamic adaptive grid mapping, and the article positioning accuracy is improved in combination with hierarchical feature matching.

The invention discloses a robot track generation method and a robot track generation system based on a Kolmogorov-Arnold network. The method comprises the following steps: extracting observation data and corresponding action data from a data source file for storing a robot movement track; the motion data is used as a diffusion model noise adding object, observation data is used as a condition to train a KAN strategy network, and the KAN strategy network is formed by fusing a Kolmogorov-Arnold network into a basic network architecture of a diffusion strategy in a modular form and is used for predicting noise to be added; the trained KAN strategy network is selected according to the field task scene requirement, predicted noise is generated according to actual observation data and action data of the robot, and the next action is generated by using the predicted noise through an inverse diffusion formula of the diffusion model. According to the method, an effective and smooth track can be generated, and the overall performance is remarkably improved.

The invention discloses an intelligent sorting and scheduling method and system for a multimodal transport scene, and relates to the technical field of warehousing, and the method comprises the steps: collecting the transportation mode identifiers of a plurality of transportation carriers, the physical attribute information of corresponding goods, and the operation state information of a three-dimensional sorting system in real time; analyzing the multimodal transport rigid rule information to generate a dynamic boundary condition of multimodal transport; importing the transportation mode identifier, the physical attribute information, the operation state information and the dynamic boundary condition into a deep reinforcement learning model; outputting a goods shelf distribution strategy for dynamically distributing storage levels according to the goods gravity center parameters and an equipment task sequence for controlling the AGV carrying robot to move; and a sorting execution instruction is automatically generated so as to correspondingly drive the three-dimensional sorting system to carry out goods sorting. According to the method, a'secure storage-time coupling-space cooperation 'technical closed loop is formed, and the comprehensive logistics cost of a multimodal transport full link is remarkably reduced.

The invention discloses a biped robot gait generation method based on enhanced confrontation motion prior, and belongs to the technical field of robot control. Comprising the following steps: constructing an enhanced PPO-AMP network; establishing an action network model, an evaluation network model and an AMP module; establishing a biped robot motion model, and selecting a simulation experiment environment; performing reinforcement learning on the enhanced PPO-AMP network by using a reward function combining task rewards and style rewards and combining an improved PPO-Clip mechanism with KL divergence constraint on the basis of a simulation experiment environment to obtain optimized network parameters; the optimized enhanced PPO-AMP network is used for generating the biped robot gait meeting the actual walking task. According to the control method, the environment perception capability is optimized, the network structure is deepened, and the reward mechanism and the training strategy are improved, so that the biped robot has high robustness and high natural gaits on various complex terrains at the same time.

The invention discloses an intelligent plate grabbing robot operation action monitoring control system, and relates to the technical field of robot action monitoring control. Comprising the following steps: data acquisition: installing a sensor at an execution end of the intelligent plate grabbing robot, collecting shape data of a plate in real time, and collecting positions of surrounding obstacles at the same time to obtain plate shape data and obstacle shape data; by constructing a dynamic grabbing scheme and a template library, the system can effectively cope with plates of different shapes and materials and achieve a targeted grabbing strategy, so that the grabbing efficiency is improved, meanwhile, the problems in the grabbing process can be rapidly recognized, the grabbing strategy is automatically adjusted, the requirement for human intervention is lowered, the autonomy of the system is improved, and the system is suitable for popularization and application. And meanwhile, various data in the grabbing process can be collected and analyzed in real time, instant feedback and adjustment of grabbing actions are ensured, and the operation flexibility and accuracy are improved.

The invention relates to the technical field of large-scale structure measurement, and discloses a robot laser vision three-dimensional scanning measurement system for a large-scale structural member. The system comprises a robot motion platform, a laser vision sensing module, an ambient light sensing module, a point cloud processing module and a motion control module, wherein the laser vision sensing module is arranged at the tail end of the robot motion platform. The ambient light sensing module collects ambient light intensity distribution data in real time, and the motion control module combines the data with three-dimensional model data of the large-scale structural member to be measured to generate a multi-view dynamic scanning path instruction. The robot motion platform executes an instruction to drive the laser vision sensing module to move, the module projects laser stripes to the surface of a to-be-measured structural member and synchronously collects an image, the point cloud processing module performs adaptive filtering processing on the image and then generates three-dimensional point cloud data, and the requirement for efficient and accurate measurement of a large structural member in a complex industrial environment can be met.

The invention provides a robot motion path planning method and device based on machine learning, and relates to the technical field of robot path planning, and the method comprises the steps: obtaining and storing the position information of an obstacle and a target point in an environment; constructing an artificial potential field according to the current position of the robot, the speed of the robot, the target point position, the target speed, the obstacle information and the motion state of the robot; the artificial potential field is optimized through a variable neighborhood search algorithm, and a reinforcement learning strategy is introduced into the variable neighborhood search algorithm; nodes on the basic path generated by the artificial potential field method serve as initial starting points of ants in the ant colony algorithm, and a final path is obtained through multi-round iterative optimization through an ant release pheromone mechanism, a transition probability selection mechanism and a pheromone volatilization updating mechanism; and evaluating the optimized path and dynamically adjusting the path. The robot motion path planning method can cope with complex environment changes, and effectively improves the safety and efficiency of path planning.

The invention relates to the field of intelligent robot control, and discloses an intelligent security robot control method comprising the following steps: building a robot motion and task model: building a dynamic relationship among the position, speed and acceleration of an intelligent security robot, and defining a multi-task target executed by the robot, wherein the execution time, the energy consumption and the priority of each task are expressed through a target function; the invention also discloses a control system of the intelligent security robot, and the system comprises a control decision module which is used for operating the multi-objective optimization function and carrying out the scheduling and path planning of multiple tasks executed by the robot, and an environment perception module which is used for obtaining the state information of the environment where the robot is located. According to the method, a dynamic optimization control strategy of the HJB equation is introduced, so that the robot can adjust paths and task scheduling in real time, dynamic environment changes are effectively coped with, and efficient and flexible task execution and real-time optimization in a complex environment are achieved.

The invention relates to a welding pose track generation method of a workpiece fillet weld, a medium and equipment. The method comprises the following steps: acquiring a two-dimensional workpiece weld point; performing three-dimensional reconstruction on the two-dimensional workpiece welding seam points to obtain a three-dimensional workpiece welding seam point cloud; a plurality of geometric equations in the three-dimensional workpiece welding seam point cloud are extracted, track regeneration is conducted according to the geometric equations, and an initial welding track and key welding seam point coordinates are obtained; and attitude mapping is conducted according to the prior welding attitude and the key welding seam point coordinates, interpolation calculation is conducted according to the initial welding track, and a final welding pose track is obtained. The problems that in the prior art, the actual space pose of a workpiece cannot be sensed online, the three-dimensional contour of a fillet weld cannot be automatically recognized, and the motion trail of a robot cannot be generated in real time are solved.

The invention discloses a humanoid robot walking and running unified control method and system based on a biological rhythm, and belongs to the technical field of humanoid robots. Capturing human walking and running action data, and extracting rhythm features from frequency and phase time domains; constructing a rhythm generator based on the rhythm features extracted in the step 1; according to a strategy gradient method based on a constraint reinforcement learning algorithm, a walking and running unified control strategy is constructed, rhythm information generated based on a rhythm generator is adopted in the walking and running unified control strategy, then action-critic is used for training, a bionic motion control system Walk2Run under speed driving is jointly constructed, natural gait transition and frequency adjustment are achieved, and the walking and running unified control strategy is obtained. And meanwhile, the physical capacity limitation of the robot is met, and motion fluency and energy efficiency are improved. The problems that an existing humanoid robot does not achieve energy optimization of human walking during walking, and mobility of an existing data set has high requirements for the motion ability of the robot and is poor in generalization ability are solved.

The invention relates to the technical field of robot control, and discloses a robot motion control method, device, equipment and medium, and the method comprises the steps: constructing a simulation environment and configuring a robot model, obtaining a depth image of an area in front of the robot model through a depth sensor, carrying out the semantic segmentation of the depth image, generating a semantic mask of a passable area, and carrying out the recognition of the passable area. And generating three-dimensional terrain point cloud data, extracting a semantic feature vector of the point cloud data, inputting the semantic feature vector and the body state information of the robot model into the motion control strategy model to generate a joint control instruction, and driving the robot model to move according to the joint control instruction. According to the method, depth image perception, semantic segmentation, three-dimensional point cloud reconstruction and feature extraction are fused, robot body state information is combined, a self-adaptive joint control instruction is generated, dynamic perception, decision making and control of robot motion in various complex terrains can be achieved, and the autonomous motion energy of the robot in an unstructured environment is effectively improved.

The invention provides a multi-view video reward mechanism learning framework and method, and the framework comprises a multi-view video evaluation subsystem which employs a multi-view video learning framework MVR to evaluate robot behaviors according to multi-view video learning; the visual feedback reward feedback strategy subsystem is used for generating visual feedback according to task text description through a visual language large model; accurate reward feedback is obtained according to the latest state correlation evaluation, so that the strategy is adjusted more effectively; the visual feedback task reward balancing subsystem analyzes the importance degree of task reward feedback according to the degree that robot behaviors approach to an expected target through a task reward model, dynamically adjusts the relative size between a task reward and a visual language model reward according to state correlation, and carries out visual feedback and task reward balancing; and the multi-view video reward combination subsystem is used for providing more accurate visual feedback and learning effect in a complex robot motion task in combination with multi-view videos and task rewards.