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Reinforcement learning for human-robot shared control
journal contribution
posted on 2023-06-09, 15:59 authored by Yanan LiYanan Li, Keng Peng Tee, Rui Yan, Shuzhi Sam GeThis paper aims at proposing a general framework of shared control for human-robot interaction. Human dynamics are considered in analysis of the coupled human-robot system. Motion intentions of both human and robot are taken into account in the control objective of the robot. Reinforcement learning is developed to achieve the control objective subject to unknown dynamics of human and robot. The closed-loop system performance is discussed through a rigorous proof. Simulations are conducted to demonstrate the learning capability of the proposed method and its feasibility in handling various situations. Compared to existing works, the proposed framework combines motion intentions of both human and robot in a human-robot shared control system, without the requirement of the knowledge of humans and robots dynamics.
History
Publication status
- Published
File Version
- Accepted version
Journal
Assembly AutomationISSN
0144-5154Publisher
EmeraldExternal DOI
Department affiliated with
- Engineering and Design Publications
Full text available
- Yes
Peer reviewed?
- Yes
Legacy Posted Date
2018-11-23First Open Access (FOA) Date
2019-10-08First Compliant Deposit (FCD) Date
2018-11-22Usage metrics
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