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Applied Bionics and Biomechanics
Volume 5, Issue 4, Pages 195-211

Behaviour Generation in Humanoids by Learning Potential-Based Policies from Constrained Motion

Matthew Howard,1 Stefan Klanke,1 Michael Gienger,2 Christian Goerick,2 and Sethu Vijayakumar1

1School of Informatics, University of Edinburgh, Edinburgh EH9 3JZ, UK
2Honda Research Institute Europe GmbH, Offenbach/Main D-63073, Germany

Received 1 February 2009

Copyright © 2008 Hindawi Publishing Corporation. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Movement generation that is consistent with observed or demonstrated behaviour is an efficient way to seed movement planning in complex, high-dimensional movement systems like humanoid robots. We present a method for learning potential-based 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.