Reinforcement learning for improving imitated in-contact skills

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A4 Artikkeli konferenssijulkaisussa
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Date

2017-01-02

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Department of Electrical Engineering and Automation

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Language

en

Pages

17

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16th IEEE-RAS International Conference on Humanoid Robots, Humanoids 2016, pp. 194-201, IEEE-RAS International Conference on Humanoid Robots

Abstract

Although motor primitives (MPs) for trajectory basedskills have been studied extensively, much less attentionhas been devoted to studying in-contact tasks. With robotsbecoming more commonplace, it is both economical andconvenient to have a mechanism for learning an in-contacttask from demonstration. However, transferring an in-contactskill such as wood planing from a human to a robot issignificantly more challenging than transferring a trajectory basedskill; it requires a simultaneous control of both poseand force. Furthermore, some in-contact tasks have extremelycomplex contact environments. We present a framework for imitating an in-contact skill from a human demonstrationand automatically enhancing the imitated force profile usinga policy search method. The framework encodes both the thedemonstrated trajectory and the normal contact force usingDynamic Movement Primitives (DMPs). In experiments, we utilizePolicy Improvement with Path Integral (PI2) algorithm forupdating the imitated force policy. Our results demonstrate theeffectiveness of this approach in improving the performanceof a wood planing task. After only two update rounds, all theupdated policies have outperformed the imitated policy at asignificance level of P < 0.001.

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Keywords

learning from demonstration, programming by demonstration, in-contact task, in-contact, skill optimization, reinforcement learning

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DOI

10.1109/HUMANOIDS.2016.7803277

Citation

Hazara, M & Kyrki, V 2017, Reinforcement learning for improving imitated in-contact skills. in 16th IEEE-RAS International Conference on Humanoid Robots, Humanoids 2016., 7803277, IEEE-RAS International Conference on Humanoid Robots, IEEE, pp. 194-201, IEEE-RAS International Conference on Humanoid Robots, Cancun, Mexico, 15/11/2016. https://doi.org/10.1109/HUMANOIDS.2016.7803277

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https://urn.fi/URN:NBN:fi:aalto-201612165983

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[article-cris] Sähkötekniikan korkeakoulu / ELEC