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Behaviour generation in humanoids by learning potential-based policies from constrained motion.pdf1.91 MBAdobe PDFView/Open
Title: Behaviour Generation in Humanoids by Learning Potential-based Policies from Constrained Motion
Authors: Howard, Matthew
Klanke, Stefan
Gienger, Michael
Goerick, Christian
Vijayakumar, Sethu
Issue Date: Dec-2008
Journal Title: Applied Bionics and Biomechanics
Volume: 5
Issue: 4
Page Numbers: 195-211
Publisher: Taylor and Francis
Abstract: Movement generation that is consistent with observed or demonstrated behaviour is an efficient way to seed movement planning in complex, high-dimensionalmovement systems like humanoid robots.We present a method for learning potentialbased policies from constrained motion data. In contrast to previous approaches to direct policy learning, our method can combine observations from a variety of contexts where different constraints are in force, to learn the underlying unconstrained policy in form of its potential function. This allows us to generalise and predict behaviour where novel constraints apply. We demonstrate our approach on systems of varying complexity, including kinematic data from the ASIMO humanoid robot with 22 degrees of freedom.
Keywords: imitation learning
constrained motion
ISSN: 1176-2322
Appears in Collections:Informatics Publications

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