Robotic impedance learning for robot-assisted physical training

Li, Y., Zhou, X., Zhong, J. ORCID: 0000-0001-7642-2961 and Li, X., 2019. Robotic impedance learning for robot-assisted physical training. Frontiers in Robotics and AI, 6: 78. ISSN 2296-9144

14819_1735a_Zhong.pdf - Published version

Download (2MB) | Preview


Impedance control has been widely used in robotic applications where a robot has physical interaction with its environment. However, how the impedance parameters are adapted according to the context of a task is still an open problem. In this paper, we focus on a physical training scenario, where the robot needs to adjust its impedance parameters according to the human user's performance so as to promote their learning. This is a challenging problem as humans' dynamic behaviors are difficult to model and subject to uncertainties. Considering that physical training usually involves a repetitive process, we develop impedance learning in physical training by using iterative learning control (ILC). Since the condition of the same iteration length in traditional ILC cannot be met due to human variance, we adopt a novel ILC to deal with varying iteration lengthes. By theoretical analysis and simulations, we show that the proposed method can effectively learn the robot's impedance in the application of robot-assisted physical training.

Item Type: Journal article
Publication Title: Frontiers in Robotics and AI
Creators: Li, Y., Zhou, X., Zhong, J. and Li, X.
Publisher: Frontiers Research Foundation
Date: 2019
Volume: 6
ISSN: 2296-9144
Rights: Copyright © 2019 Li, Zhou, Zhong and Li. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Divisions: Schools > School of Science and Technology
Record created by: Jonathan Gallacher
Date Added: 12 Sep 2019 12:48
Last Modified: 04 Oct 2019 10:20

Actions (login required)

Edit View Edit View


Views per month over past year


Downloads per month over past year