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Robot hand-eye cooperation based on improved inverse reinforcement learning

Ning Yu (Key Laboratory of Networked Control Systems, Chinese Academy of Sciences, Beijing, China) (Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Beijing, China) (Shenyang Institute of Automation Chinese Academy of Sciences, Shenyang, China) (University of the Chinese Academy of Sciences, Beijing, China)
Lin Nan (Key Laboratory of Networked Control Systems, Chinese Academy of Sciences, Beijing, China) (Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Beijing, China) (Shenyang Institute of Automation Chinese Academy of Sciences, Shenyang, China)
Tao Ku (Key Laboratory of Networked Control Systems, Chinese Academy of Sciences, Beijing, China) (Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Beijing, China) (Shenyang Institute of Automation Chinese Academy of Sciences, Shenyang, China)

Industrial Robot

ISSN: 0143-991x

Article publication date: 30 November 2021

Issue publication date: 30 June 2022

128

Abstract

Purpose

How to make accurate action decisions based on visual information is one of the important research directions of industrial robots. The purpose of this paper is to design a highly optimized hand-eye coordination model of the robot to improve the robots’ on-site decision-making ability.

Design/methodology/approach

The combination of inverse reinforcement learning (IRL) algorithm and generative adversarial network can effectively reduce the dependence on expert samples and robots can obtain the decision-making performance that the degree of optimization is not lower than or even higher than that of expert samples.

Findings

The performance of the proposed model is verified in the simulation environment and real scene. By monitoring the reward distribution of the reward function and the trajectory of the robot, the proposed model is compared with other existing methods. The experimental results show that the proposed model has better decision-making performance in the case of less expert data.

Originality/value

A robot hand-eye cooperation model based on improved IRL is proposed and verified. Empirical investigations on real experiments reveal that overall, the proposed approach tends to improve the real efficiency by more than 10% when compared to alternative hand-eye cooperation methods.

Keywords

Acknowledgements

Authors wish to express their appreciation to the reviewers for their helpful suggestions which greatly improved the presentation of this paper.

Funding statement: The work was supposed by the National Key Research and Development Program of China under Grant(2018YFB1308801, 2020YFB1708503, 2019YFB1705003).

Citation

Yu, N., Nan, L. and Ku, T. (2022), "Robot hand-eye cooperation based on improved inverse reinforcement learning", Industrial Robot, Vol. 49 No. 5, pp. 877-884. https://doi.org/10.1108/IR-09-2021-0208

Publisher

:

Emerald Publishing Limited

Copyright © 2021, Emerald Publishing Limited

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