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
Source PublicationROBOTICS AND AUTONOMOUS SYSTEMS
ISSN0921-8890
2020-08-01
Volume130Pages:9
Corresponding AuthorLiu, Zhiyong(zhiyong.liu@ia.ac.cn)
AbstractRobotic 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.
KeywordSkill transfer learning Serial structures Robot skill learning Graph Neural Network
DOI10.1016/j.robot.2020.103555
Indexed BySCI
Language英语
Funding ProjectNational 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]
Funding OrganizationNational 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 Research AreaAutomation & Control Systems ; Computer Science ; Robotics
WOS SubjectAutomation & Control Systems ; Computer Science, Artificial Intelligence ; Robotics
WOS IDWOS:000538810400003
PublisherELSEVIER
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/39775
Collection复杂系统管理与控制国家重点实验室_机器人理论与应用
Corresponding AuthorLiu, Zhiyong
Affiliation1.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
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
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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