In this article,a robot skills learning framework is developed,which considers both motion modeling and execution.In order to enable the robot to learn skills from demonstrations,a learning method called dynamic movem...In this article,a robot skills learning framework is developed,which considers both motion modeling and execution.In order to enable the robot to learn skills from demonstrations,a learning method called dynamic movement primitives(DMPs)is introduced to model motion.A staged teaching strategy is integrated into DMPs frameworks to enhance the generality such that the complicated tasks can be also performed for multi-joint manipulators.The DMP connection method is used to make an accurate and smooth transition in position and velocity space to connect complex motion sequences.In addition,motions are categorized into different goals and durations.It is worth mentioning that an adaptive neural networks(NNs)control method is proposed to achieve highly accurate trajectory tracking and to ensure the performance of action execution,which is beneficial to the improvement of reliability of the skills learning system.The experiment test on the Baxter robot verifies the effectiveness of the proposed method.展开更多
As a wearable robot,an exoskeleton provides a direct transfer of mechanical power to assist or augment the wearer’s movement with an anthropomorphic configuration.When an exoskeleton is used to facilitate the wearer...As a wearable robot,an exoskeleton provides a direct transfer of mechanical power to assist or augment the wearer’s movement with an anthropomorphic configuration.When an exoskeleton is used to facilitate the wearer’s movement,a motion generation process often plays an important role in high-level control.One of the main challenges in this area is to generate in real time a reference trajectory that is parallel with human intention and can adapt to different situations.In this paper,we first describe a novel motion modeling method based on probabilistic movement primitive(ProMP)for a lower limb exoskeleton,which is a new and powerful representative tool for generating motion trajectories.To adapt the trajectory to different situations when the exoskeleton is used by different wearers,we propose a novel motion learning scheme based on black-box optimization(BBO)PIBB combined with ProMP.The motion model is first learned by ProMP offline,which can generate reference trajectories for use by exoskeleton controllers online.PIBB is adopted to learn and update the model for online trajectory generation,which provides the capability of adaptation of the system and eliminates the effects of uncertainties.Simulations and experiments involving six subjects using the lower limb exoskeleton HEXO demonstrate the effectiveness of the proposed methods.展开更多
Dynamic movement primitives(DMPs)as a robust and efcient framework has been studied widely for robot learning from demonstration.Classical DMPs framework mainly focuses on the movement learning in Cartesian or joint s...Dynamic movement primitives(DMPs)as a robust and efcient framework has been studied widely for robot learning from demonstration.Classical DMPs framework mainly focuses on the movement learning in Cartesian or joint space,and can’t properly represent end-efector orientation.In this paper,we present an extended DMPs framework(EDMPs)both in Cartesian space and 2-Dimensional(2D)sphere manifold for Quaternion-based orientation learning and generalization.Gaussian mixture model and Gaussian mixture regression(GMM-GMR)are adopted as the initialization phase of EDMPs to handle multi-demonstrations and obtain their mean and covariance.Additionally,some evaluation indicators including reachability and similarity are defned to characterize the learning and generalization abilities of EDMPs.Finally,a real-world experiment was conducted with human demonstrations,the endpoint poses of human arm were recorded and successfully transferred from human to the robot.The experimental results show that the absolute errors of the Cartesian and Riemannian space skills are less than 3.5 mm and 1.0°,respectively.The Pearson’s correlation coefcients of the Cartesian and Riemannian space skills are mostly greater than 0.9.The developed EDMPs exhibits superior reachability and similarity for the multi-space skills’learning and generalization.This research proposes a fused framework with EDMPs and GMM-GMR which has sufcient capability to handle the multi-space skills in multi-demonstrations.展开更多
Articulated movements are fundamental in many human and robotic tasks.While humans can learn and generalise arbitrarily long sequences of movements,and particularly can optimise them to ft the constraints and features...Articulated movements are fundamental in many human and robotic tasks.While humans can learn and generalise arbitrarily long sequences of movements,and particularly can optimise them to ft the constraints and features of their body,robots are often programmed to execute point-to-point precise but fxed patterns.This study proposes a new approach to interpreting and reproducing articulated and complex trajectories as a set of known robot-based primitives.Instead of achieving accurate reproductions,the proposed approach aims at interpreting data in an agent-centred fashion,according to an agent s primitive movements.The method improves the accuracy of a reproduction with an incremental process that seeks frst a rough approximation by capturing the most essential features of a demonstrated trajectory.Observing the discrepancy between the demonstrated and reproduced trajectories,the process then proceeds with incremental decompositions and new searches in sub-optimal parts of the trajectory.The aim is to achieve an agent-centred interpretation and progressive learning that fts in the frst place the robots capability,as opposed to a data-centred decomposition analysis.Tests on both geometric and human generated trajectories reveal that the use of own primitives results in remarkable robustness and generalisation properties of the method.In particular,because trajectories are understood and abstracted by means of agent-optimised primitives,the method has two main features: 1) Reproduced trajectories are general and represent an abstraction of the data.2) The algorithm is capable of reconstructing highly noisy or corrupted data without pre-processing thanks to an implicit and emergent noise suppression and feature detection.This study suggests a novel bio-inspired approach to interpreting,learning and reproducing articulated movements and trajectories.Possible applications include drawing,writing,movement generation,object manipulation,and other tasks where the performance requires human-like interpretation and generalisation capabilities.展开更多
Personalized gait curves are generated to enhance patient adaptability to gait trajectories used for passive training in the early stage of rehabilitation for hemiplegic patients.The article utilizes the random forest...Personalized gait curves are generated to enhance patient adaptability to gait trajectories used for passive training in the early stage of rehabilitation for hemiplegic patients.The article utilizes the random forest algorithm to construct a gait parameter model,which maps the relationship between parameters such as height,weight,age,gender,and gait speed,achieving prediction of key points on the gait curve.To enhance prediction accuracy,an attention mechanism is introduced into the algorithm to focus more on the main features.Meanwhile,to ensure high similarity between the reconstructed gait curve and the normal one,probabilistic motion primitives(ProMP)are used to learn the probability distribution of normal gait data and construct a gait trajectorymodel.Finally,using the specified step speed as input,select a reference gait trajectory from the learned trajectory,and reconstruct the curve of the reference trajectoryusing the gait keypoints predictedby the parametermodel toobtain the final curve.Simulation results demonstrate that the method proposed in this paper achieves 98%and 96%curve correlations when generating personalized lower limb gait curves for different patients,respectively,indicating its suitability for such tasks.展开更多
基金National Natural Science Foundation of China(Nos.62225304,92148204 and 62061160371)National Key Research and Development Program of China(Nos.2021ZD0114503 and 2019YFB1703600)Beijing Top Discipline for Artificial Intelligence Science and Engineering,University of Science and Technology Beijing,and the Beijing Natural Science Foundation(No.JQ20026).
文摘In this article,a robot skills learning framework is developed,which considers both motion modeling and execution.In order to enable the robot to learn skills from demonstrations,a learning method called dynamic movement primitives(DMPs)is introduced to model motion.A staged teaching strategy is integrated into DMPs frameworks to enhance the generality such that the complicated tasks can be also performed for multi-joint manipulators.The DMP connection method is used to make an accurate and smooth transition in position and velocity space to connect complex motion sequences.In addition,motions are categorized into different goals and durations.It is worth mentioning that an adaptive neural networks(NNs)control method is proposed to achieve highly accurate trajectory tracking and to ensure the performance of action execution,which is beneficial to the improvement of reliability of the skills learning system.The experiment test on the Baxter robot verifies the effectiveness of the proposed method.
基金Project supported by the National Natural Science Foundation of China(No.U21A20120)。
文摘As a wearable robot,an exoskeleton provides a direct transfer of mechanical power to assist or augment the wearer’s movement with an anthropomorphic configuration.When an exoskeleton is used to facilitate the wearer’s movement,a motion generation process often plays an important role in high-level control.One of the main challenges in this area is to generate in real time a reference trajectory that is parallel with human intention and can adapt to different situations.In this paper,we first describe a novel motion modeling method based on probabilistic movement primitive(ProMP)for a lower limb exoskeleton,which is a new and powerful representative tool for generating motion trajectories.To adapt the trajectory to different situations when the exoskeleton is used by different wearers,we propose a novel motion learning scheme based on black-box optimization(BBO)PIBB combined with ProMP.The motion model is first learned by ProMP offline,which can generate reference trajectories for use by exoskeleton controllers online.PIBB is adopted to learn and update the model for online trajectory generation,which provides the capability of adaptation of the system and eliminates the effects of uncertainties.Simulations and experiments involving six subjects using the lower limb exoskeleton HEXO demonstrate the effectiveness of the proposed methods.
基金Supported by National Natural Science Foundation of China(Grant No.52175029)Key Industrial Chain Projects of Shaanxi Province(Grant No.2018ZDCXL-GY-06-05).
文摘Dynamic movement primitives(DMPs)as a robust and efcient framework has been studied widely for robot learning from demonstration.Classical DMPs framework mainly focuses on the movement learning in Cartesian or joint space,and can’t properly represent end-efector orientation.In this paper,we present an extended DMPs framework(EDMPs)both in Cartesian space and 2-Dimensional(2D)sphere manifold for Quaternion-based orientation learning and generalization.Gaussian mixture model and Gaussian mixture regression(GMM-GMR)are adopted as the initialization phase of EDMPs to handle multi-demonstrations and obtain their mean and covariance.Additionally,some evaluation indicators including reachability and similarity are defned to characterize the learning and generalization abilities of EDMPs.Finally,a real-world experiment was conducted with human demonstrations,the endpoint poses of human arm were recorded and successfully transferred from human to the robot.The experimental results show that the absolute errors of the Cartesian and Riemannian space skills are less than 3.5 mm and 1.0°,respectively.The Pearson’s correlation coefcients of the Cartesian and Riemannian space skills are mostly greater than 0.9.The developed EDMPs exhibits superior reachability and similarity for the multi-space skills’learning and generalization.This research proposes a fused framework with EDMPs and GMM-GMR which has sufcient capability to handle the multi-space skills in multi-demonstrations.
基金supported by European Community s Seventh Framework Programme FP7/2007-2013,Challenge 2,Cognitive Systems,Interaction,Robotics(No.248311AMARSi)
文摘Articulated movements are fundamental in many human and robotic tasks.While humans can learn and generalise arbitrarily long sequences of movements,and particularly can optimise them to ft the constraints and features of their body,robots are often programmed to execute point-to-point precise but fxed patterns.This study proposes a new approach to interpreting and reproducing articulated and complex trajectories as a set of known robot-based primitives.Instead of achieving accurate reproductions,the proposed approach aims at interpreting data in an agent-centred fashion,according to an agent s primitive movements.The method improves the accuracy of a reproduction with an incremental process that seeks frst a rough approximation by capturing the most essential features of a demonstrated trajectory.Observing the discrepancy between the demonstrated and reproduced trajectories,the process then proceeds with incremental decompositions and new searches in sub-optimal parts of the trajectory.The aim is to achieve an agent-centred interpretation and progressive learning that fts in the frst place the robots capability,as opposed to a data-centred decomposition analysis.Tests on both geometric and human generated trajectories reveal that the use of own primitives results in remarkable robustness and generalisation properties of the method.In particular,because trajectories are understood and abstracted by means of agent-optimised primitives,the method has two main features: 1) Reproduced trajectories are general and represent an abstraction of the data.2) The algorithm is capable of reconstructing highly noisy or corrupted data without pre-processing thanks to an implicit and emergent noise suppression and feature detection.This study suggests a novel bio-inspired approach to interpreting,learning and reproducing articulated movements and trajectories.Possible applications include drawing,writing,movement generation,object manipulation,and other tasks where the performance requires human-like interpretation and generalisation capabilities.
基金supported by Guizhou Provincial Department of Science and Technology(Guizhou Science and Technology Cooperation Support[2021]General 442)Guizhou Provincial Department of Science and Technology(Guizhou Science and Technology Cooperation Support[2023]General 179)Guizhou Provincial Department of Science and Technology(Guizhou Science and Technology Cooperation Support[2023]General 096).
文摘Personalized gait curves are generated to enhance patient adaptability to gait trajectories used for passive training in the early stage of rehabilitation for hemiplegic patients.The article utilizes the random forest algorithm to construct a gait parameter model,which maps the relationship between parameters such as height,weight,age,gender,and gait speed,achieving prediction of key points on the gait curve.To enhance prediction accuracy,an attention mechanism is introduced into the algorithm to focus more on the main features.Meanwhile,to ensure high similarity between the reconstructed gait curve and the normal one,probabilistic motion primitives(ProMP)are used to learn the probability distribution of normal gait data and construct a gait trajectorymodel.Finally,using the specified step speed as input,select a reference gait trajectory from the learned trajectory,and reconstruct the curve of the reference trajectoryusing the gait keypoints predictedby the parametermodel toobtain the final curve.Simulation results demonstrate that the method proposed in this paper achieves 98%and 96%curve correlations when generating personalized lower limb gait curves for different patients,respectively,indicating its suitability for such tasks.