Waypoints updating based on Adam and ILC for path learning in physical human-robot interaction
Jingkang Xia, Chenjian Song, Deqing Huang, Xueyan Xing, Lei Ma, Yanan Li
Abstract
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.
BibTeX
@inproceedings{icra2021_waypointsupdatin,
title = {Waypoints updating based on Adam and ILC for path learning in physical human-robot interaction},
author = {Jingkang Xia and Chenjian Song and Deqing Huang and Xueyan Xing and Lei Ma and Yanan Li},
booktitle = {ICRA 2021},
year = {2021}
}