Applied Bionics and Biomechanics

Applied Bionics and Biomechanics / 2008 / Article

Open Access

Volume 5 |Article ID 316371 |

Matthew Howard, Stefan Klanke, Michael Gienger, Christian Goerick, Sethu Vijayakumar, "Behaviour Generation in Humanoids by Learning Potential-Based Policies from Constrained Motion", Applied Bionics and Biomechanics, vol. 5, Article ID 316371, 17 pages, 2008.

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

Received01 Feb 2009


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.

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.

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