Duan, A, Camoriano, R, Ferigo, D et al. (4 more authors) (2019) Learning to Sequence Multiple Tasks with Competing Constraints. In: 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IROS 2019, 04-08 Nov 2019, Macau, China. IEEE , pp. 2672-2678. ISBN 978-1-7281-4004-9
Abstract
Imitation learning offers a general framework where robots can efficiently acquire novel motor skills from demonstrations of a human teacher. While many promising achievements have been shown, the majority of them are only focused on single-stroke movements, without taking into account the problem of multi-tasks sequencing. Conceivably, sequencing different atomic tasks can further augment the robot's capabilities as well as avoid repetitive demonstrations. In this paper, we propose to address the issue of multi-tasks sequencing with emphasis on handling the so-called competing constraints, which emerge due to the existence of the concurrent constraints from Cartesian and joint trajectories. Specifically, we explore the null space of the robot from an information-theoretic perspective in order to maintain imitation fidelity during transition between consecutive tasks. The effectiveness of the proposed method is validated through simulated and real experiments on the iCub humanoid robot.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | ©2019 IEEE. This is an author produced version of a paper published in 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | humanoid robots , human-robot interaction , learning (artificial intelligence) |
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: | 01 Jun 2020 11:59 |
Last Modified: | 01 Jun 2020 11:59 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/IROS40897.2019.8968496 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:159904 |