Behaviour generation in humanoids by learning potential-based policies from constrained motion

Article type: Research Article

Authors: Howard, Matthew | Klanke, Stefan | Gienger, Michael | Goerick, Christian | Vijayakumar, Sethu

Affiliations: School of Informatics, University of Edinburgh, Edinburgh EH9 3JZ, United Kingdom | Honda Research Institute Europe GmbH, Offenbach/Main D-63073, Germany

Note: [] Corresponding author. E-mail: [email protected]

Abstract: 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.

Keywords: imitation learning, constrained motion, kinematics and dynamics

DOI: 10.1080/11762320902789830

Journal: Applied Bionics and Biomechanics, vol. 5, no. 4, pp. 195-211, 2008