Robot skill learning and the data dilemma it faces: a systematic review
Robotic Intelligence and Automation
ISSN: 2754-6969
Article publication date: 13 March 2024
Issue publication date: 6 May 2024
Abstract
Purpose
Compared with traditional methods relying on manual teaching or system modeling, data-driven learning methods, such as deep reinforcement learning and imitation learning, show more promising potential to cope with the challenges brought by increasingly complex tasks and environments, which have become the hot research topic in the field of robot skill learning. However, the contradiction between the difficulty of collecting robot–environment interaction data and the low data efficiency causes all these methods to face a serious data dilemma, which has become one of the key issues restricting their development. Therefore, this paper aims to comprehensively sort out and analyze the cause and solutions for the data dilemma in robot skill learning.
Design/methodology/approach
First, this review analyzes the causes of the data dilemma based on the classification and comparison of data-driven methods for robot skill learning; Then, the existing methods used to solve the data dilemma are introduced in detail. Finally, this review discusses the remaining open challenges and promising research topics for solving the data dilemma in the future.
Findings
This review shows that simulation–reality combination, state representation learning and knowledge sharing are crucial for overcoming the data dilemma of robot skill learning.
Originality/value
To the best of the authors’ knowledge, there are no surveys that systematically and comprehensively sort out and analyze the data dilemma in robot skill learning in the existing literature. It is hoped that this review can be helpful to better address the data dilemma in robot skill learning in the future.
Keywords
Acknowledgements
This work was supported by the National Key Research and Development Program of China under Grant 2020AAA0108905; in part by the National Natural Science Foundation of China under Grant 51975415, Grant 61825303, Grant 62088101, Grant U2013602; in part by the Science and Technology Commission of Shanghai Municipal Project under Grant 2021SHZDZX0100, Grant 22ZR1467100; and in part by the Fundamental Research Funds for the Central Universities under Grant 22120210547.
The authors are with the Department of Control Science and Engineering, Tongji University, Shanghai 201804, China and also with the Frontiers Science Center for Intelligent Autonomous Systems, Shanghai 201210, China (e-mail: 1810357@tongji.edu.cn; hebin@tongji.edu.cn; wangzhipeng@tongji.edu.cn; 2011600@tongji.edu.cn; jasonsang@tongji.edu.cn; yanmin.zhou@tongji.edu.cn).
Citation
Jiang, R., He, B., Wang, Z., Cheng, X., Sang, H. and Zhou, Y. (2024), "Robot skill learning and the data dilemma it faces: a systematic review", Robotic Intelligence and Automation, Vol. 44 No. 2, pp. 270-286. https://doi.org/10.1108/RIA-10-2023-0146
Publisher
:Emerald Publishing Limited
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