Skill Learning Strategy Based on Dynamic Motion Primitives for Human-Robot Cooperative Manipulation

• Published in: IEEE transactions on cognitive and developmental systems Vol. 13; no. 1; pp. 105 - 117
• Main Authors: Li, Junjun, Li, Zhijun, Li, Xinde, Feng, Ying, Hu, Yingbai, Xu, Bugong
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.03.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: 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
• ISSN: 2379-8920; 2379-8939
• DOI: 10.1109/TCDS.2020.3021762

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.