Knowledge Commons of Institute of Automation,CAS
WAGNN: A Weighted Aggregation Graph Neural Network for robot skill learning | |
Zhang, Fengyi1,2; Liu, Zhiyong1,2,3; Xiong, Fangzhou1,2; Su, Jianhua1; Qiao, Hong1,2,3 | |
发表期刊 | ROBOTICS AND AUTONOMOUS SYSTEMS |
ISSN | 0921-8890 |
2020-08-01 | |
卷号 | 130页码:9 |
摘要 | Robotic skill learning suffers from the diversity and complexity of robotic tasks in continuous domains, making the learning of transferable skills one of the most challenging issues in this area, especially for the case where robots differ in terms of structure. Aiming at making the policy easier to be generalized or transferred, the graph neural networks (GNN) was previously employed to incorporate explicitly the robot structure into the policy network. In this paper, with the help of graph neural networks, we further investigate the problem of efficient learning transferable policies for robots with serial structure, which commonly appears in various robot bodies, such as robotic arms and the leg of centipede. Based on a kinematics analysis on the serial robotic structure, the policy network is improved by proposing a weighted information aggregation strategy. It is experimentally shown on different robotics structures that in a few-shot policy learning setting, the new aggregation strategy significantly improves the performance not only on the learning speed, but also on the control accuracy. (C) 2020 Elsevier B.V. All rights reserved. |
关键词 | Skill transfer learning Serial structures Robot skill learning Graph Neural Network |
DOI | 10.1016/j.robot.2020.103555 |
收录类别 | SCI |
语种 | 英语 |
资助项目 | National Key Research and Development Plan of China[2017YFB1300202] ; NSFC, China[U1613213] ; NSFC, China[61375005] ; NSFC, China[61503383] ; NSFC, China[61210009] ; Strategic Priority Research Program of Chinese Academy of Science[XDB32050100] ; Dongguan core technology research frontier project, China[2019622101001] |
项目资助者 | National Key Research and Development Plan of China ; NSFC, China ; Strategic Priority Research Program of Chinese Academy of Science ; Dongguan core technology research frontier project, China |
WOS研究方向 | Automation & Control Systems ; Computer Science ; Robotics |
WOS类目 | Automation & Control Systems ; Computer Science, Artificial Intelligence ; Robotics |
WOS记录号 | WOS:000538810400003 |
出版者 | ELSEVIER |
七大方向——子方向分类 | 强化与进化学习 |
国重实验室规划方向分类 | 其他 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/39775 |
专题 | 多模态人工智能系统全国重点实验室_机器人理论与应用 |
通讯作者 | Liu, Zhiyong |
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 UCAS, Sch Artificial Intelligence, Beijing 100049, Peoples R China 3.Chinese Acad Sci, CAS Ctr Excellence Brain Sci & Intelligence Techn, 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 | Zhang, Fengyi,Liu, Zhiyong,Xiong, Fangzhou,et al. WAGNN: A Weighted Aggregation Graph Neural Network for robot skill learning[J]. ROBOTICS AND AUTONOMOUS SYSTEMS,2020,130:9. |
APA | Zhang, Fengyi,Liu, Zhiyong,Xiong, Fangzhou,Su, Jianhua,&Qiao, Hong.(2020).WAGNN: A Weighted Aggregation Graph Neural Network for robot skill learning.ROBOTICS AND AUTONOMOUS SYSTEMS,130,9. |
MLA | Zhang, Fengyi,et al."WAGNN: A Weighted Aggregation Graph Neural Network for robot skill learning".ROBOTICS AND AUTONOMOUS SYSTEMS 130(2020):9. |
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