Behaviour Generation in Humanoids by Learning Potential-based Policies from Constrained Motion
Matthew Howard, Stefan Klanke, Michael Gienger, Christian Görick and Sethu Vijayakumar
Journal of Applied Bionics and Biomechanics
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.