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Whom Will an Intrinsically Motivated Robot Learner Choose to Imitate from?

Nguyen, Sao Mai ; Oudeyer, Pierre-Yves

This paper studies an interactive learning system that couples internally guided learning and social interaction in the case it can interact with several teachers. Socially Guided Intrinsic Motivation with Interactive learning at the Meta level (SGIMIM) is an algorithm for robot learning of motor skills in highdimensional, continuous and non-preset environments, with two levels of active learning: SGIM-IM actively decides at a metalevel when and to whom to ask for help; and an active choice of goals in autonomous exploration. We illustrate through an air hockey game that SGIM-IM efficiently chooses the best strategy.

Schlagwörter: Active Learning , Intrinsic Motivation , Social Learning , Programming by Demonstration , Imitation
Beteiligte Einrichtung: Forschungsinstitut für Kognition und Robotik (CoR-Lab)
DDC-Sachgruppe: Datenverarbeitung, Informatik

Sao Mai Nguyen, Pierre-Yves Oudeyer, Whom Will an Intrinsically Motivated Robot Learner Choose to Imitate from?. Proceedings of the Post-Graduate Conference on Robotics and Development of Cognition, J. Szufnarowska, Ed., September 2012

URL: http://biecoll.ub.uni-bielefeld.de/volltexte/2012/5238

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 Letzte Änderung: 15.2.2011
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