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A Learning Framework of Adaptive Manipulative Skills From Human to Robot
Yang, Chenguang1; Zeng, Chao1; Cong Y(丛杨)2; Wang, Ning3; Wang M(王敏)1
Department机器人学研究室
Source PublicationIEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
ISSN1551-3203
2019
Volume15Issue:2Pages:1153-1161
Indexed BySCI
WOS IDWOS:000458199000051
Contribution Rank2
KeywordAdaptive stiffness control human-robot skill transfer skill segmentation stiffness generalization
AbstractRobots 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.
Language英语
WOS SubjectAutomation & Control Systems ; Computer Science, Interdisciplinary Applications ; Engineering, Industrial
WOS Research AreaAutomation & Control Systems ; Computer Science ; Engineering
Funding ProjectNational 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]
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Cited Times:5[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/24249
Collection机器人学研究室
Corresponding AuthorYang, Chenguang
Affiliation1.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
Recommended Citation
GB/T 7714
Yang, Chenguang,Zeng, Chao,Cong Y,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 Yang, Chenguang,Zeng, Chao,Cong Y,Wang, Ning,&Wang M.(2019).A Learning Framework of Adaptive Manipulative Skills From Human to Robot.IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS,15(2),1153-1161.
MLA Yang, Chenguang,et al."A Learning Framework of Adaptive Manipulative Skills From Human to Robot".IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 15.2(2019):1153-1161.
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