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Original Research ARTICLE Provisionally accepted The full-text will be published soon. Notify me

Front. Robot. AI | doi: 10.3389/frobt.2019.00078

Robotic Impedance Learning for Robot-assisted Physical Training

  • 1University of Sussex, United Kingdom
  • 2Beijing Institute of Control Engineering, China Academy of Space Technology, China
  • 3Nottingham Trent University, United Kingdom
  • 4Imperial College London, United Kingdom

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.

Keywords: impedance learning, Impedance Control, iterative learning control, physical training, robotic control

Received: 07 May 2019; Accepted: 08 Aug 2019.

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.

* Correspondence: Dr. Xiaodong Zhou, Beijing Institute of Control Engineering, China Academy of Space Technology, Haidian, China, xdzhou.buaa@gmail.com