Impedance learning for human-guided robots in contact with unknown environments

Xing, Xueyan; Burdet, Etienne; Si, Weiyong; Yang, Chenguang; Li, Yanan

Abstract

Previous works have developed impedance control to increase safety and improve performance in contact tasks, where the robot is in physical interaction with either an environment or a human user. This article investigates impedance learning for a robot guided by a human user while interacting with an unknown environment. We develop automatic adaptation of robot impedance parameters to reduce the effort required to guide the robot through the environment, while guaranteeing interaction stability. For nonrepetitive tasks, this novel adaptive controller can attenuate disturbances by learning appropriate robot impedance. Implemented as an iterative learning controller, it can compensate for position dependent disturbances in repeated movements. Experiments demonstrate that the robot controller can, in both repetitive and nonrepetitive tasks: first, identify and compensate for the interaction, second, ensure both contact stability (with reduced tracking error) and maneuverability (with less driving effort of the human user) in contact with real environments, and third, is superior to previous velocity-based impedance adaptation control methods.

• Journal Article Type: Article
• Acceptance Date: Apr 28, 2023
• Online Publication Date: Jun 23, 2023
• Publication Date: Oct 31, 2023
• Deposit Date: Jun 26, 2023
• Publicly Available Date: Jun 28, 2023
• Journal: IEEE Transactions on Robotics
• Print ISSN: 1552-3098
• Electronic ISSN: 1941-0468
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Volume: 39
• Issue: 5
• Pages: 3705 - 3721
• DOI: https://doi.org/10.1109/tro.2023.3281483
• Keywords: Human–robot–environment interaction control, impedance control, learning control
• Public URL: https://uwe-repository.worktribe.com/output/10888902
• Publisher URL: https://ieeexplore.ieee.org/document/10160165