Learning from demonstration(LfD)is an appealing method of helping robots learn new skills.Numerous papers have presented methods of LfD with good performance in robotics.However,complicated robot tasks that need to ca...Learning from demonstration(LfD)is an appealing method of helping robots learn new skills.Numerous papers have presented methods of LfD with good performance in robotics.However,complicated robot tasks that need to carefully regulate path planning strategies remain unanswered.Contact or non-contact constraints in specific robot tasks make the path planning problem more difficult,as the interaction between the robot and the environment is time-varying.In this paper,we focus on the path planning of complex robot tasks in the domain of LfD and give a novel perspective for classifying imitation learning and inverse reinforcement learning.This classification is based on constraints and obstacle avoidance.Finally,we summarize these methods and present promising directions for robot application and LfD theory.展开更多
This paper proposes a novel approach for physical human-robot interactions(pHRI), where a robot provides guidance forces to a user based on the user performance. This framework tunes the forces in regards to behavior ...This paper proposes a novel approach for physical human-robot interactions(pHRI), where a robot provides guidance forces to a user based on the user performance. This framework tunes the forces in regards to behavior of each user in coping with different tasks, where lower performance results in higher intervention from the robot. This personalized physical human-robot interaction(p2HRI) method incorporates adaptive modeling of the interaction between the human and the robot as well as learning from demonstration(LfD) techniques to adapt to the users' performance. This approach is based on model predictive control where the system optimizes the rendered forces by predicting the performance of the user. Moreover, continuous learning of the user behavior is added so that the models and personalized considerations are updated based on the change of user performance over time. Applying this framework to a field such as haptic guidance for skill improvement, allows a more personalized learning experience where the interaction between the robot as the intelligent tutor and the student as the user,is better adjusted based on the skill level of the individual and their gradual improvement. The results suggest that the precision of the model of the interaction is improved using this proposed method,and the addition of the considered personalized factors to a more adaptive strategy for rendering of guidance forces.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
To automate heavy-duty hydraulic manipulators in construction applications,trajectory learning from demonstration is increasingly in demand.However,it faces difficulties in motion noise owing to factors such as size s...To automate heavy-duty hydraulic manipulators in construction applications,trajectory learning from demonstration is increasingly in demand.However,it faces difficulties in motion noise owing to factors such as size scaling and oscillation tendency.A smooth trajectory learning method is established to overcome this problem by segmenting the demonstration and extracting the subgoals for motion noise cancellation.The imperfect demonstration trajectory is segmented by clustering the end-effector’s velocity in the task space with locally weighted noise cancellation to reduce the impact of velocity fluctuations.A sequentially hierarchical Dirichlet process algorithm with temporal encoding is designed to extract the intended subgoals and filter inefficient operations.Then,the learned trajectory is reconstructed,combined with dynamic motion primitives(DMP).The comparison test results indicate that the proposed method can learn a relevant trajectory that reflects the real intention of the user from an imperfect demonstration.Taking DMP and Sparse Sampling as comparisons,two cases of automatic trajectory tracking tasks are performed,which shows that the average position error with respect to the reference can be reduced because inefficient operations or movements are effectively filtered.展开更多
基金supported by the National Natural Science Foundation of China(Grant No.91848202)the Foundation for Innovative Research Groups of the National Natural Science Foundation of China(Grant No.51521003)。
文摘Learning from demonstration(LfD)is an appealing method of helping robots learn new skills.Numerous papers have presented methods of LfD with good performance in robotics.However,complicated robot tasks that need to carefully regulate path planning strategies remain unanswered.Contact or non-contact constraints in specific robot tasks make the path planning problem more difficult,as the interaction between the robot and the environment is time-varying.In this paper,we focus on the path planning of complex robot tasks in the domain of LfD and give a novel perspective for classifying imitation learning and inverse reinforcement learning.This classification is based on constraints and obstacle avoidance.Finally,we summarize these methods and present promising directions for robot application and LfD theory.
文摘This paper proposes a novel approach for physical human-robot interactions(pHRI), where a robot provides guidance forces to a user based on the user performance. This framework tunes the forces in regards to behavior of each user in coping with different tasks, where lower performance results in higher intervention from the robot. This personalized physical human-robot interaction(p2HRI) method incorporates adaptive modeling of the interaction between the human and the robot as well as learning from demonstration(LfD) techniques to adapt to the users' performance. This approach is based on model predictive control where the system optimizes the rendered forces by predicting the performance of the user. Moreover, continuous learning of the user behavior is added so that the models and personalized considerations are updated based on the change of user performance over time. Applying this framework to a field such as haptic guidance for skill improvement, allows a more personalized learning experience where the interaction between the robot as the intelligent tutor and the student as the user,is better adjusted based on the skill level of the individual and their gradual improvement. The results suggest that the precision of the model of the interaction is improved using this proposed method,and the addition of the considered personalized factors to a more adaptive strategy for rendering of guidance forces.
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 in part by the National Natural Science Foundation of China(Grant Nos.52322503 and 52075055)in part by the Natural Science Foundation of Chongqing,China(Grant No.2024NSCQ-JQX0123)+1 种基金in part by the Major Scientific and Technological Research and Development Project of Jiangxi Province,China(Grant No.20233AAE02001)in part by the Independent Research Project of State Key Laboratory of Mechanical Transmission for Advanced Equipment,China(Grant No.SKLMT-ZZKT-2024R02).
文摘To automate heavy-duty hydraulic manipulators in construction applications,trajectory learning from demonstration is increasingly in demand.However,it faces difficulties in motion noise owing to factors such as size scaling and oscillation tendency.A smooth trajectory learning method is established to overcome this problem by segmenting the demonstration and extracting the subgoals for motion noise cancellation.The imperfect demonstration trajectory is segmented by clustering the end-effector’s velocity in the task space with locally weighted noise cancellation to reduce the impact of velocity fluctuations.A sequentially hierarchical Dirichlet process algorithm with temporal encoding is designed to extract the intended subgoals and filter inefficient operations.Then,the learned trajectory is reconstructed,combined with dynamic motion primitives(DMP).The comparison test results indicate that the proposed method can learn a relevant trajectory that reflects the real intention of the user from an imperfect demonstration.Taking DMP and Sparse Sampling as comparisons,two cases of automatic trajectory tracking tasks are performed,which shows that the average position error with respect to the reference can be reduced because inefficient operations or movements are effectively filtered.
