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Robotic impedance learning for robot-assisted physical training

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Version 2 2023-06-07, 08:27
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journal contribution
posted on 2023-06-07, 08:27 authored by Yanan LiYanan Li, Xiaodong Zhou, Junpei Zhong, Xuefang Li
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 behaviours 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.

History

Publication status

  • Published

File Version

  • Published version

Journal

Frontiers in Robotics and AI

ISSN

2296-9144

Publisher

Frontiers Media

Issue

a78

Volume

6

Page range

1-13

Department affiliated with

  • Engineering and Design Publications

Research groups affiliated with

  • Dynamics, Control and Vehicle Research Group Publications

Notes

This research was supported by National Natural Science Foundation of China (Grant No. 51805025).

Full text available

  • No

Peer reviewed?

  • Yes

Legacy Posted Date

2019-08-16

First Open Access (FOA) Date

2019-09-06

First Compliant Deposit (FCD) Date

2019-08-15

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