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  Reinforcement Learning to adjust Robot Movements to New Situations

Kober, J., Oztop, E., & Peters, J. (2011). Reinforcement Learning to adjust Robot Movements to New Situations. In T. Walsh (Ed.), Twenty-Second International Joint Conference on Artificial Intelligence (IJCAI 2011) (pp. 2650-2655). Menlo Park, CA, USA: AAAI Press.

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Datensatz-Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-BB2C-A Versions-Permalink: https://hdl.handle.net/21.11116/0000-0002-0A9D-3
Genre: Konferenzbeitrag

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https://www.ijcai.org/Proceedings/2011/ (Verlagsversion)
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Urheber

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 Urheber:
Kober, J1, 2, 3, Autor           
Oztop, E, Autor
Peters, J1, 2, 3, Autor           
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society, ou_1497647              
3Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

Inhalt

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Schlagwörter: -
 Zusammenfassung: Many complex robot motor skills can be represented using elementary movements, and there exist efficient techniques for learning parametrized motor plans using demonstrations and self-improvement. However with current techniques, in many cases, the robot currently needs to learn a new elementary movement even if a parametrized motor plan exists that covers a related situation. A method is needed that modulates the elementary movement through the meta-parameters of its representation. In this paper, we describe how to learn such mappings from circumstances to meta-parameters using reinforcement learning. In particular we use a kernelized version of the reward-weighted regression. We show two robot applications of the presented setup in robotic domains; the generalization of throwing movements in darts, and of hitting movements in table tennis. We demonstrate that both tasks can be learned successfully using simulated and real robots.

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Sprache(n):
 Datum: 2011-07
 Publikationsstatus: Erschienen
 Seiten: -
 Ort, Verlag, Ausgabe: -
 Inhaltsverzeichnis: -
 Art der Begutachtung: -
 Identifikatoren: BibTex Citekey: KoberOP2011
 Art des Abschluß: -

Veranstaltung

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Titel: Twenty-Second International Joint Conference on Artificial Intelligence (IJCAI 2011)
Veranstaltungsort: Barcelona, Spain
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Entscheidung

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Projektinformation

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Quelle 1

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Titel: Twenty-Second International Joint Conference on Artificial Intelligence (IJCAI 2011)
Genre der Quelle: Konferenzband
 Urheber:
Walsh, T, Herausgeber
Affiliations:
-
Ort, Verlag, Ausgabe: Menlo Park, CA, USA : AAAI Press
Seiten: - Band / Heft: - Artikelnummer: - Start- / Endseite: 2650 - 2655 Identifikator: ISBN: 978-1-57735-512-0