A neural network based framework for variable impedance skills learning from demonstrations

doi:10.1016/j.robot.2022.104312

CORC > 自动化研究所 > 中国科学院自动化研究所 > 多模态人工智能系统全国重点实验室

	A neural network based framework for variable impedance skills learning from demonstrations
	Zhang, Yu 2,3; Cheng, Long 2,3; Cao, Ran 2,3; Li, Houcheng 2,3; Yang, Chenguang 1
刊名	ROBOTICS AND AUTONOMOUS SYSTEMS
	2023-02-01
卷号	160 页码:10
关键词	Variable impedance skill Learning from demonstrations Skills learning Human-robot interaction
ISSN号	0921-8890
DOI	10.1016/j.robot.2022.104312
通讯作者	Cheng, Long(Long.cheng@ia.ac.cn)
英文摘要	Robots are becoming standard collaborators not only in factories, hospitals, and offices, but also in people's homes, where they can play an important role in situations where a human cannot complete a task alone or needs the help of another person (i.e., collaborative tasks). Variable impedance control with contact forces is critical for robots to successfully perform such manipulation tasks, and robots should be equipped with adaptive capabilities because conditions vary significantly for different robotic tasks in dynamic environments. This can be achieved by learning human motion capabilities and variable impedance skills. In this paper, a neural-network-based framework for learning variable impedance skills is proposed. The proposed approach builds the full stiffness function with the acquired forces and position learned from demonstrations, and then is used together with the sensed data to achieve the variable impedance control. The proposed algorithm can adapt to unknown situations that change the learned motion skill as needed (e.g., adapt to intermediate via-points or avoid obstacles). The proposed framework consists of two parts: Learning motion features and learning impedance features. The motion features learning is validated by reproducing, generalizing, and adapting to transit points and avoiding obstacles in the LASA dataset. Impedance features learning is validated based on a virtual variable stiffness system that achieves higher accuracy (approximately 90%) compared to traditional methods in a manual dataset, and the whole framework is validated through a co-manipulation task between a person and the Franka Emika robot.(c) 2022 Elsevier B.V. All rights reserved.
资助项目	National Natural Science Foundation of China ; Beijing Municipal Natural Science Foundation ; [62025307] ; [U1913209] ; [JQ19020]
WOS关键词	ROBOT ; MOTIONS
WOS研究方向	Automation & Control Systems ; Computer Science ; Robotics
语种	英语
出版者	ELSEVIER
WOS记录号	WOS:000903974100006
资助机构	National Natural Science Foundation of China ; Beijing Municipal Natural Science Foundation
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/51153]
专题	多模态人工智能系统全国重点实验室
通讯作者	Cheng, Long
作者单位	1.Univ West England, Bristol Robot Lab, Bristol BS16 1QY, England 2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 3.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Zhang, Yu,Cheng, Long,Cao, Ran,et al. A neural network based framework for variable impedance skills learning from demonstrations[J]. ROBOTICS AND AUTONOMOUS SYSTEMS,2023,160:10.
APA	Zhang, Yu,Cheng, Long,Cao, Ran,Li, Houcheng,&Yang, Chenguang.(2023).A neural network based framework for variable impedance skills learning from demonstrations.ROBOTICS AND AUTONOMOUS SYSTEMS,160,10.
MLA	Zhang, Yu,et al."A neural network based framework for variable impedance skills learning from demonstrations".ROBOTICS AND AUTONOMOUS SYSTEMS 160(2023):10.