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Hierarchical Policy Learning With Demonstration Learning for Robotic Multiple Peg-in-Hole Assembly Tasks

文献类型：期刊论文

作者Yan, Shaohua1,2; Xu, De1,2; Tao, Xian1,2
刊名IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
出版日期2023-10-01
卷号19期号:10页码:10254-10264
ISSN号1551-3203
关键词Assembly model demonstration learning (DL) force-based control algorithm hierarchical reinforcement learning (HRL) peg-in-hole assembly
DOI10.1109/TII.2023.3240936
通讯作者Xu, De(de.xu@ia.ac.cn)
英文摘要The force-based control algorithm of robotic multiple peg-in-hole assembly is a challenge. For the difficulty of low adaptability of model-based control algorithms and low learning efficiency of model-free control algorithms, a goal-based hierarchical policy learning (HPL) algorithm that combines conventional control algorithm and demonstration learning (DL) algorithm is proposed to learn the assembly skill. First, the goal-based HPL algorithm adds goal as a new variable to the action value function. Multiple states reached in each episode are randomly selected as subgoals to improve the distribution of positive rewards. Second, an initial policy that combines conventional control algorithm and DL algorithm is designed. The combined coefficient of these two algorithms is learned by HPL algorithm. Finally, a conical surface is used to compute the forces and moments of simplified assembly simulation model. Our algorithm is well implemented in both simulation and real-world environments. The experimental results verify the effectiveness of the proposed method.
WOS关键词EFFICIENT INSERTION
资助项目National Natural Science Foundation of China[62273341] ; Beijing Municipal Natural Science Foundation[4212044] ; Beijing Municipal Natural Science Foundation[TII-22-2698]
WOS研究方向Automation & Control Systems ; Computer Science ; Engineering
语种英语
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号WOS:001047436000028
资助机构National Natural Science Foundation of China ; Beijing Municipal Natural Science Foundation
源URL[http://ir.ia.ac.cn/handle/173211/54071]  
专题中科院工业视觉智能装备工程实验室
通讯作者Xu, De
作者单位1.Chinese Acad Sci, Inst Automat, Res Ctr Precis Sensing & Control, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China
推荐引用方式
GB/T 7714		 Yan, Shaohua,Xu, De,Tao, Xian. Hierarchical Policy Learning With Demonstration Learning for Robotic Multiple Peg-in-Hole Assembly Tasks[J]. IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS,2023,19(10):10254-10264.
APA Yan, Shaohua,Xu, De,&Tao, Xian.(2023).Hierarchical Policy Learning With Demonstration Learning for Robotic Multiple Peg-in-Hole Assembly Tasks.IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS,19(10),10254-10264.
MLA Yan, Shaohua,et al."Hierarchical Policy Learning With Demonstration Learning for Robotic Multiple Peg-in-Hole Assembly Tasks".IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 19.10(2023):10254-10264.

入库方式： OAI收割

来源：自动化研究所

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