Skill Learning Strategy Based on Dynamic Motion Primitives for Human-Robot Cooperative Manipulation
This article presents a skill learning-based hierarchical control strategy for human-robot cooperative manipulation, which constitutes a novel learning-control system. The high-level learning strategy aims to learn the motor skills from human demonstrations by fusion with dynamic motion primitives (...
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| Published in | IEEE transactions on cognitive and developmental systems Vol. 13; no. 1; pp. 105 - 117 |
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| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Piscataway
IEEE
01.03.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subjects | |
| Online Access | Get full text |
| ISSN | 2379-8920 2379-8939 |
| DOI | 10.1109/TCDS.2020.3021762 |
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| Abstract | This article presents a skill learning-based hierarchical control strategy for human-robot cooperative manipulation, which constitutes a novel learning-control system. The high-level learning strategy aims to learn the motor skills from human demonstrations by fusion with dynamic motion primitives (DMPs) and the Gaussian mixture model (GMM). The lower level control strategy guarantees the compliance of the robot movement under human interaction using admittance control and integral barrier Lyapunov function (IBLF)-based adaptive neural controller. First, the robot learns the motor skills from observing the successful execution of tasks by a demonstrator through DMP-GMM methods. Then, the robot reproduces the complex skills and executes the interactive task by demonstrations. Finally, the effectiveness of the proposed learning-control strategy is demonstrated with experimental results. The results show that the developed hierarchical strategy has good performance in cooperation by learning and control that reacts compliantly to robot interaction with human subjects. |
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| AbstractList | This article presents a skill learning-based hierarchical control strategy for human–robot cooperative manipulation, which constitutes a novel learning-control system. The high-level learning strategy aims to learn the motor skills from human demonstrations by fusion with dynamic motion primitives (DMPs) and the Gaussian mixture model (GMM). The lower level control strategy guarantees the compliance of the robot movement under human interaction using admittance control and integral barrier Lyapunov function (IBLF)-based adaptive neural controller. First, the robot learns the motor skills from observing the successful execution of tasks by a demonstrator through DMP-GMM methods. Then, the robot reproduces the complex skills and executes the interactive task by demonstrations. Finally, the effectiveness of the proposed learning-control strategy is demonstrated with experimental results. The results show that the developed hierarchical strategy has good performance in cooperation by learning and control that reacts compliantly to robot interaction with human subjects. |
| Author | Li, Zhijun Hu, Yingbai Li, Junjun Xu, Bugong Li, Xinde Feng, Ying |
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| SubjectTerms | Adaptive control Admittance Admittance control Aerospace electronics dynamic motion primitives (DMPs) Electrical impedance exoskeleton robot Exoskeletons Gaussian mixture model (GMM) Human motion human–robot cooperative manipulation integral barrier Lyapunov function (IBLF) Interactive control Learning Liapunov functions Motor ability Probabilistic models Robot control Robot dynamics Robot motion Robots Skills Strategy Task analysis Trajectory |
| Title | Skill Learning Strategy Based on Dynamic Motion Primitives for Human-Robot Cooperative Manipulation |
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