Implementation of assembly task based on guided policy search algorithm
Dong QW(董青卫)1,3,4,5; Zang CZ(臧传治)2; Zeng P(曾鹏)1,3,4; Wan GX(万广喜)1,3,4,5; He YP(贺云鹏)1,3,4,5; Dong XT(董晓婷)1,3,4,5
2021
会议日期October 13-16, 2021
会议地点Toronto, ON, Canada
关键词Assembly Tasks Reinforcement Learning Robot Policy Search
页码1-6
英文摘要At present, safely solving complex and high-precision assembly tasks in an unstructured environment is still an unresolved challenge. The development of artificial intelligence technology provides new ideas for robots in unstructured scenes to autonomously solve the problem of contact-rich peg-in-hole tasks. In this paper, we construct the shaft hole assembly task as a reinforcement learning problem, explore the change of the convergence rate when adding force and torque information in the state space, and evaluate the performance of the guided policy search algorithm on the shaft hole assembly task. We also compared the changes in the force and torque feedback from sensors at the beginning and end of learning. The experiment proves that we can complete the specific shaft hole assembly task by learning, and also shows the effectiveness of using the force and torque information when the peg and hole parts are in contact.
源文献作者IEEE Ind Elect Soc
产权排序1
会议录IECON 2021 - 47TH ANNUAL CONFERENCE OF THE IEEE INDUSTRIAL ELECTRONICS SOCIETY
会议录出版者IEEE
会议录出版地New York
语种英语
ISSN号1553-572X
ISBN号978-1-6654-3554-3
WOS记录号WOS:000767230604055
内容类型会议论文
源URL[http://ir.sia.cn/handle/173321/30822]  
专题沈阳自动化研究所_工业控制网络与系统研究室
通讯作者Zang CZ(臧传治)
作者单位1.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China
2.Shenyang University of Technology, Shenyang 110870, China
3.Key Laboratory of Networked Control Systems, Chinese Academy of Sciences, Shenyang 110016, China
4.Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
5.University of Chinese Academy of Sciences, Beijing 100049, China
推荐引用方式
GB/T 7714
Dong QW,Zang CZ,Zeng P,et al. Implementation of assembly task based on guided policy search algorithm[C]. 见:. Toronto, ON, Canada. October 13-16, 2021.
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