A Learning Framework of Adaptive Manipulative Skills From Human to Robot

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	A Learning Framework of Adaptive Manipulative Skills From Human to Robot
	Zeng, Chao 1; Wang, Ning 3; Wang M(王敏)1 ; Yang, Chenguang 1; Cong Y(丛杨)2
刊名	IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
	2019-02-01
卷号	15 期号:2 页码:1153-1161
关键词	Adaptive stiffness control human-robot skill transfer skill segmentation stiffness generalization
ISSN号	1551-3203
产权排序	2
英文摘要	Robots are often required to generalize the skills learned from human demonstrations to fulfil new task requirements. However, skill generalization will be difficult to realize when facing with the following situations: the skill for a complex multistep task includes a number of features; some special constraints are imposed on the robots during the process of task reproduction; and a completely new situation quite different with the one in which demonstrations are given to the robot. This work proposes a new framework to facilitate robot skill generalization. The basic idea lies in that the learned skills are first segmented into a sequence of subskills automatically, then each individual subskill is encoded and regulated accordingly. Specifically, we adapt each set of the segmented movement trajectories individually instead of the whole movement profiles, thus, making it more convenient for the realization of skill generalization. In addition, human limb stiffness estimated from surface electromyographic signals is considered in the framework for the realization of human-to-robot variable impedance control skill transfer, as well as the generalization of both movement trajectories and stiffness profiles. Experimental study has been performed to verify the effectiveness of the proposed framework.
资助项目	National Nature Science Foundation (NSFC)[61473120] ; National Nature Science Foundation (NSFC)[61773169] ; National Nature Science Foundation (NSFC)[U1613214] ; Science and Technology Planning Project of Guangzhou[201607010006] ; State Key Laboratory of Robotics and System (HIT)[SKLRS-2017-KF-13] ; Fundamental Research Funds for the Central Universities[2017ZD057]
WOS研究方向	Automation & Control Systems ; Computer Science ; Engineering
语种	英语
WOS记录号	WOS:000458199000051
资助机构	National Nature Science Foundation (NSFC) ; Science and Technology Planning Project of Guangzhou ; State Key Laboratory of Robotics and System (HIT) ; Fundamental Research Funds for the Central Universities
内容类型	期刊论文
源URL	[http://ir.sia.cn/handle/173321/24249]
专题	沈阳自动化研究所_机器人学研究室
通讯作者	Yang, Chenguang
作者单位	1.Key Laboratory of Autonomous Systems and Networked Control, College of Automation Science and Engineering, South China University of Technology, Guangzhou 510640, China 2.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China 3.Center for Robotics and Neural Systems, Plymouth University, Plymouth PL4 8AA, UK
推荐引用方式 GB/T 7714	Zeng, Chao,Wang, Ning,Wang M,et al. A Learning Framework of Adaptive Manipulative Skills From Human to Robot[J]. IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS,2019,15(2):1153-1161.
APA	Zeng, Chao,Wang, Ning,Wang M,Yang, Chenguang,&Cong Y.(2019).A Learning Framework of Adaptive Manipulative Skills From Human to Robot.IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS,15(2),1153-1161.
MLA	Zeng, Chao,et al."A Learning Framework of Adaptive Manipulative Skills From Human to Robot".IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 15.2(2019):1153-1161.