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Human-robot force cooperation analysis by deep reinforcement learning

Shaodong Li (Guangxi Key Laboratory of Intelligent Control and Maintenance of Power Equipment, Guangxi University, Nanning, China)
Xiaogang Yuan (Guangxi Key Laboratory of Intelligent Control and Maintenance of Power Equipment, Guangxi University, Nanning, China)
Hongjian Yu (State Key Laboratory of Robotics and Systems, Harbin Institute of Technology, Harbin, China)

Industrial Robot

ISSN: 0143-991x

Article publication date: 1 December 2022

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Abstract

Purpose

This study aims to realize natural and effort-saving motion behavior and improve effectiveness for different operators in human–robot force cooperation.

Design/methodology/approach

The parameter of admittance model is identified by deep deterministic policy gradient (DDPG) to realize human–robot force cooperation for different operators in this paper. The movement coupling problem of hybrid robot is solved by realizing position and pose drags. In DDPG, minimum jerk trajectory is selected as the reward objective function, and the variable prioritized experience replay is applied to balance the exploration and exploitation.

Findings

A series of simulations are implemented to validate the superiority and stability of DDPG. Furthermore, three sets of experiments involving mass parameter, damping parameter and DDPG are implemented, the effect of DDPG in real environment is validated and could meet the cooperation demand for different operators.

Originality/value

DDPG is applied in admittance model identification to realize human–robot force cooperation for different operators. And minimum jerk trajectory is introduced into reward objective to meet requirement of human arm free movements. The algorithm proposed in this paper could be further extended in the other operation task.

Keywords

Acknowledgements

Conflict of Interests: The authors declare that they have no conflict of interest.

Code or data availability Not applicable.

Ethics approval Not applicable.

Consent to participate All authors consent to participate.

Consent for publication All authors consent for publication.

Authors contribution: Shaodong Li proposed the human–robot cooperation algorithm based on DDPG; Xiaogang Yuan finished the simulation and experiment in manuscript; Hongjian Yu provided the experimental platform support and reviewed the manuscript.

Citation

Li, S., Yuan, X. and Yu, H. (2022), "Human-robot force cooperation analysis by deep reinforcement learning", Industrial Robot, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/IR-05-2022-0135

Publisher

:

Emerald Publishing Limited

Copyright © 2022, Emerald Publishing Limited

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