Sun, C., Cohn, A.G. orcid.org/0000-0002-7652-8907 and Leonetti, M. (2023) Online Human Capability Estimation Through Reinforcement Learning and Interaction. In: 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 01-05 Oct 2023, Detroit, MI, USA. IEEE , pp. 7984-7991. ISBN 9781665491907
Abstract
Service robots are expected to assist users in a constantly growing range of environments and tasks. People may be unique in many ways, and online adaptation of robots is central to personalized assistance. We focus on collaborative tasks in which the human collaborator may not be fully ablebodied, with the aim for the robot to automatically determine the best level of support. We propose a methodology for online adaptation based on Reinforcement Learning and Bayesian inference. As the Reinforcement Learning process continuously adjusts the robot's behavior, the actions that become part of the improved policy are used by the Bayesian inference module as local evidence of human capability, which can be generalized across the state space. The estimated capabilities are then used as pre-conditions to collaborative actions, so that the robot can quickly disable actions that the person seems unable to perform. We demonstrate and validate our approach on two simulated tasks and one real-world collaborative task across a range of motion and sensing capabilities.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | This item is protected by copyright. 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. |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 22 Feb 2024 17:11 |
Last Modified: | 22 Feb 2024 17:22 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/iros55552.2023.10341868 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:209504 |