Deutsch
 
Hilfe Datenschutzhinweis Impressum
  DetailsucheBrowse

Datensatz

DATENSATZ AKTIONENEXPORT
Zur Ablage hinzufügen
 DetailsÜbersicht
  Policy Search for Motor Primitives in Robotics

Kober, J., & Peters, J. (2009). Policy Search for Motor Primitives in Robotics. Advances in neural information processing systems 21: 22nd Annual Conference on Neural Information Processing Systems 2008, 849-856.

Item is

 

einblenden: alle ausblenden: alle

Basisdaten

einblenden: ausblenden:
Datensatz-Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-C49F-3 Versions-Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-C4A0-E
Genre: Konferenzbeitrag

Externe Referenzen

einblenden:

Urheber

einblenden:
ausblenden:
 Urheber:
Kober, J1, Autor           
Peters, J1, 2, Autor           
Koller, Herausgeber
D., Herausgeber
Schuurmans, D., Herausgeber
Bengio, Y., Herausgeber
Bottou, L., Herausgeber
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              

Inhalt

einblenden:
ausblenden:
Schlagwörter: -
 Zusammenfassung: Many motor skills in humanoid robotics can be learned using parametrized motor primitives as done in imitation learning. However, most interesting motor learning problems are high-dimensional reinforcement learning problems often beyond the reach of current methods. In this paper, we extend previous work on policy learning from the immediate reward case to episodic reinforcement learning. We show that this results into a general, common framework also connected to policy gradient methods and yielding a novel algorithm for policy learning by assuming a form of exploration that is particularly well-suited for dynamic motor primitives. The resulting algorithm is an EM-inspired algorithm applicable in complex motor learning tasks. We compare this algorithm to alternative parametrized policy search methods and show that it outperforms previous methods. We apply it in the context of motor learning and show that it can learn a complex Ball-in-a-Cup task using a real Barrett WAM robot arm.

Details

einblenden:
ausblenden:
Sprache(n):
 Datum: 2009-06
 Publikationsstatus: Erschienen
 Seiten: -
 Ort, Verlag, Ausgabe: -
 Inhaltsverzeichnis: -
 Art der Begutachtung: -
 Identifikatoren: ISBN: 978-1-605-60949-2
URI: http://nips.cc/Conferences/2008/
BibTex Citekey: 5411
 Art des Abschluß: -

Veranstaltung

einblenden:
ausblenden:
Titel: Twenty-Second Annual Conference on Neural Information Processing Systems (NIPS 2008)
Veranstaltungsort: Vancouver, BC, Canada
Start-/Enddatum: -

Entscheidung

einblenden:

Projektinformation

einblenden:

Quelle 1

einblenden:
ausblenden:
Titel: Advances in neural information processing systems 21 : 22nd Annual Conference on Neural Information Processing Systems 2008
Genre der Quelle: Zeitschrift
 Urheber:
Affiliations:
Ort, Verlag, Ausgabe: Red Hook, NY, USA : Curran
Seiten: - Band / Heft: - Artikelnummer: - Start- / Endseite: 849 - 856 Identifikator: -