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Real-Sim-Real Transfer for Real-World Robot Control Policy Learning with Deep Reinforcement Learning
Liu, Naijun1,2; Cai, Yinghao1; Lu, Tao1; Wang, Rui1,3; Wang, Shuo1,2,4
发表期刊APPLIED SCIENCES-BASEL
2020-03-01
卷号10期号:5页码:16
摘要

Compared to traditional data-driven learning methods, recently developed deep reinforcement learning (DRL) approaches can be employed to train robot agents to obtain control policies with appealing performance. However, learning control policies for real-world robots through DRL is costly and cumbersome. A promising alternative is to train policies in simulated environments and transfer the learned policies to real-world scenarios. Unfortunately, due to the reality gap between simulated and real-world environments, the policies learned in simulated environments often cannot be generalized well to the real world. Bridging the reality gap is still a challenging problem. In this paper, we propose a novel real-sim-real (RSR) transfer method that includes a real-to-sim training phase and a sim-to-real inference phase. In the real-to-sim training phase, a task-relevant simulated environment is constructed based on semantic information of the real-world scenario and coordinate transformation, and then a policy is trained with the DRL method in the built simulated environment. In the sim-to-real inference phase, the learned policy is directly applied to control the robot in real-world scenarios without any real-world data. Experimental results in two different robot control tasks show that the proposed RSR method can train skill policies with high generalization performance and significantly low training costs.

关键词robot policy learning reality gap simulated environment deep reinforcement learning
DOI10.3390/app10051555
关键词[WOS]DOMAIN ADAPTATION
收录类别SCI
语种英语
资助项目National Natural Science Foundation of China[61773378] ; National Natural Science Foundation of China[U1713222] ; National Natural Science Foundation of China[U1806204] ; Equipment Pre-Research Field Fund[61403120407] ; Opening Project of Guangdong Provincial Key Lab of Robotics and Intelligent System
项目资助者National Natural Science Foundation of China ; Equipment Pre-Research Field Fund ; Opening Project of Guangdong Provincial Key Lab of Robotics and Intelligent System
WOS研究方向Chemistry ; Engineering ; Materials Science ; Physics
WOS类目Chemistry, Multidisciplinary ; Engineering, Multidisciplinary ; Materials Science, Multidisciplinary ; Physics, Applied
WOS记录号WOS:000525298100003
出版者MDPI
七大方向——子方向分类智能机器人
引用统计
被引频次:14[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/173211/38869
专题智能机器人系统研究
通讯作者Cai, Yinghao; Lu, Tao; Wang, Shuo
作者单位1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100190, Peoples R China
3.Chinese Acad Sci, Shenzhen Inst Adv Technol, Guangdong Prov Key Lab Robot & Intelligent Syst, Shenzhen 518055, Peoples R China
4.Chinese Acad Sci, Ctr Excellence Brain Sci & Intelligence Technol, Shanghai 200031, Peoples R China
第一作者单位中国科学院自动化研究所
通讯作者单位中国科学院自动化研究所
推荐引用方式
GB/T 7714
Liu, Naijun,Cai, Yinghao,Lu, Tao,et al. Real-Sim-Real Transfer for Real-World Robot Control Policy Learning with Deep Reinforcement Learning[J]. APPLIED SCIENCES-BASEL,2020,10(5):16.
APA Liu, Naijun,Cai, Yinghao,Lu, Tao,Wang, Rui,&Wang, Shuo.(2020).Real-Sim-Real Transfer for Real-World Robot Control Policy Learning with Deep Reinforcement Learning.APPLIED SCIENCES-BASEL,10(5),16.
MLA Liu, Naijun,et al."Real-Sim-Real Transfer for Real-World Robot Control Policy Learning with Deep Reinforcement Learning".APPLIED SCIENCES-BASEL 10.5(2020):16.
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