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Efficient Insertion Control for Precision Assembly Based on Demonstration Learning and Reinforcement Learning | |
Ma, Yanqin1,2![]() ![]() ![]() | |
发表期刊 | IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
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ISSN | 1551-3203 |
2021-07-01 | |
卷号 | 17期号:7页码:4492-4502 |
通讯作者 | Xu, De(de.xu@ia.ac.cn) |
摘要 | Multiple peg-in-hole insertion control is one of the challenging tasks in precision assembly for its complex contact dynamics. In this article, an insertion policy learning method is proposed for multiple peg-in-hole precision assembly. The insertion policy learning process is separated into two phases: initial policy learning and residual policy learning. In initial policy learning, a state-to-action policy mapping model based on the Gaussian mixture model (GMM) is established. And Gaussian mixture regression (GMR) is used to generalize the policy reuse. In residual policy learning, a reinforcement learning method named normalized advantage function (NAF) is employed to refine the insertion policy via agent's exploration in the insertion environment. Moreover, an adaptive action exploration (AAE) strategy is designed to improve the performance of exploration, and the prioritized experience replay strategy is introduced to make the residual policy learning from historical experience more efficient. Besides, the hierarchical reward function is designed considering the contact dynamics as well as the efficiency and safety of precision insertion. Finally, comprehensive experiments are conducted to validate the effectiveness of the proposed insertion policy learning method. |
关键词 | Learning (artificial intelligence) Task analysis Learning systems Gaussian distribution Informatics Automation Data models Demonstration learning insertion policy learning multiple peg-in-hole insertion precision assembly reinforcement learning |
DOI | 10.1109/TII.2020.3020065 |
收录类别 | SCI |
语种 | 英语 |
资助项目 | National Natural Science Foundation of China[61873266] ; National Natural Science Foundation of China[61733004] |
项目资助者 | National Natural Science Foundation of China |
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:000638402700007 |
出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
七大方向——子方向分类 | 人工智能+制造 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/44261 |
专题 | 中国科学院工业视觉智能装备工程实验室_精密感知与控制 |
通讯作者 | Xu, De |
作者单位 | 1.Nanjing Vocat Univ Ind Technol, Sch Comp & Software, Nanjing 210023, Peoples R China 2.Chinese Acad Sci, Res Ctr Precis Sensing & Control, Inst Automat, Beijing 100190, 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 | Ma, Yanqin,Xu, De,Qin, Fangbo. Efficient Insertion Control for Precision Assembly Based on Demonstration Learning and Reinforcement Learning[J]. IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS,2021,17(7):4492-4502. |
APA | Ma, Yanqin,Xu, De,&Qin, Fangbo.(2021).Efficient Insertion Control for Precision Assembly Based on Demonstration Learning and Reinforcement Learning.IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS,17(7),4492-4502. |
MLA | Ma, Yanqin,et al."Efficient Insertion Control for Precision Assembly Based on Demonstration Learning and Reinforcement Learning".IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 17.7(2021):4492-4502. |
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