Waypoints updating based on Adam and ILC for path learning in physical human-robot interaction

Xia, Jingkang, Song, Chenjian, Huang, Deqing, Xing, Xueyan, Ma, Lei and Li, Yanan (2021) Waypoints updating based on Adam and ILC for path learning in physical human-robot interaction. IEEE International Conference on Robotics and Automation (ICRA), Xi'an, China, May 30 - June 5 2021. Published in: IEEE International Conference on Robotics and Automation (ICRA). 3359-3365. IEEE ISSN 1050-4729 ISBN 9781728190785

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This paper presents a novel method for learning and tracking of the desired path of the human partner in physical human-robot interaction. Combining the Adam optimization algorithm with iteration learning control (ILC), a path learning method is designed to generate and update reference waypoints according to the human partner’s desired path. This method firstly uses the Adam optimization algorithm to update the robot’s reference waypoints in an online manner. Then, an ILC is developed to further modify the waypoints and reduce the difference between the robot’s actual path and the human partner’s desired path in an iterative manner. Simulations and experiments on a 7-DOF Sawyer robot are carried out to show the effectiveness of our proposed method.

Item Type: Conference Proceedings
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Schools and Departments: School of Engineering and Informatics > Engineering and Design
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Date Deposited: 02 Mar 2021 09:57
Last Modified: 04 Mar 2022 17:14
URI: http://sro.sussex.ac.uk/id/eprint/97503

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Project NameSussex Project NumberFunderFunder Ref
The Game Theory of Human-Robot Interaction - HRIgameG2929EPSRC-ENGINEERING & PHYSICAL SCIENCES RESEARCH COUNCILUnset