中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 自动化研究所
  3. 中国科学院自动化研究所
  4. 多模态人工智能系统全国重点实验室
  5. 机器人应用与理论组
WAGNN: A Weighted Aggregation Graph Neural Network for robot skill learning

文献类型：期刊论文

作者Zhang, Fengyi1,2; Liu, Zhiyong1,2,3; Xiong, Fangzhou1,2; Su, Jianhua2; Qiao, Hong1,2,3
刊名ROBOTICS AND AUTONOMOUS SYSTEMS
出版日期2020-08-01
卷号130页码:9
关键词Skill transfer learning Serial structures Robot skill learning Graph Neural Network
ISSN号0921-8890
DOI10.1016/j.robot.2020.103555
通讯作者Liu, Zhiyong(zhiyong.liu@ia.ac.cn)
英文摘要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.
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]
WOS研究方向Automation & Control Systems ; Computer Science ; Robotics
语种英语
WOS记录号WOS:000538810400003
出版者ELSEVIER
资助机构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
源URL[http://ir.ia.ac.cn/handle/173211/39774]  
专题自动化研究所_复杂系统管理与控制国家重点实验室_机器人应用与理论组
通讯作者Liu, Zhiyong
作者单位1.Univ Chinese Acad Sci UCAS, Sch Artificial Intelligence, Beijing 100049, Peoples R China
2.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
3.Chinese Acad Sci, CAS Ctr Excellence Brain Sci & Intelligence Techn, Shanghai 200031, Peoples R China
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.

入库方式： OAI收割

来源：自动化研究所

浏览0
下载0
收藏0
其他版本

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。