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

doi:10.1109/TII.2023.3240936

	Hierarchical Policy Learning With Demonstration Learning for Robotic Multiple Peg-in-Hole Assembly Tasks
	Yan, Shaohua1,2 ; Xu, De1,2 ; Tao, Xian1,2
发表期刊	IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
ISSN	1551-3203
	2023-10-01
卷号	19 期号:10 页码:10254-10264
通讯作者	Xu, De(de.xu@ia.ac.cn)
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.
关键词	Assembly model demonstration learning (DL) force-based control algorithm hierarchical reinforcement learning (HRL) peg-in-hole assembly
DOI	10.1109/TII.2023.3240936
关键词[WOS]	EFFICIENT INSERTION
收录类别	SCI
语种	英语
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]
项目资助者	National Natural Science Foundation of China ; Beijing Municipal Natural Science Foundation
WOS研究方向	Automation & Control Systems ; Computer Science ; Engineering
WOS类目	Automation & Control Systems ; Computer Science, Interdisciplinary Applications ; Engineering, Industrial
WOS记录号	WOS:001047436000028
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：1[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/54072
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
第一作者单位	精密感知与控制研究中心
通讯作者单位	精密感知与控制研究中心
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.