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Path learning in human-robot collaboration tasks using iterative learning methods
journal contribution
posted on 2023-06-10, 01:41 authored by XUEYAN XING, Jingkang Xia, Deqing Huang, Yanan LiYanan LiIn a repetitive human-robot collaboration (HRC) task, robots typically are required to learn the intended motion of the human user to improve the collaboration efficiency. However, the human user's trajectory is of uncertainty when repeating the same task (e.g., human hand tremor and uncertain movement speed), which may directly deteriorate the learning performance. To address this issue, a path characterized by spatial correlation parameters, is of necessity to be learned by robots so that the aforementioned time-related uncertainty will be avoided. In this article, based on the path parameterization, a gradient-based iterative path learning (IPL) strategy is designed for the robot to learn the desired path of human. The proposed IPL strategy draws on the iterative learning methods with a properly designed performance index. Since the gradient of the performance index is hard to directly obtain in HRC, two learning methods with gradient search (GS) and gradient estimation (GE) are developed. The GS estimates the gradient online and has an advantage of easy implementation. By contrast, the advantage of GS is more obvious when the number of learned parameters is considerable due to its high learning efficiency. With these two methods, the unknown path parameters can be iteratively updated toward the desired values. To verify the effectiveness of the proposed IPL algorithm, experiments are carried out. In the experiment, a comparison between GS and GE methods is made to display their respective advantages. Besides, the proposed IPL is compared with an existing trajectory learning method subject to two different kinds of uncertainties and its better learning performance verifies its stability and capability in dealing with uncertainty.
Funding
The Game Theory of Human-Robot Interaction - HRIgame; G2929; EPSRC-ENGINEERING & PHYSICAL SCIENCES RESEARCH COUNCIL
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- Published
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- Accepted version
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IEEE Transactions on Control Systems TechnologyISSN
1063-6536Publisher
IEEEExternal DOI
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1-14Department affiliated with
- Engineering and Design Publications
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© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Full text available
- Yes
Peer reviewed?
- Yes
Legacy Posted Date
2021-11-10First Open Access (FOA) Date
2021-11-10First Compliant Deposit (FCD) Date
2021-11-10Usage metrics
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