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Movement generation that is consistent with observed or demonstrated behaviour is an efficient way to seed movement\udplanning in complex, high-dimensionalmovement systems like humanoid robots.We present a method for learning potentialbased\udpolicies from constrained motion data. In contrast to previous approaches to direct policy learning, our method can\udcombine observations from a variety of contexts where different constraints are in force, to learn the underlying unconstrained\udpolicy in form of its potential function. This allows us to generalise and predict behaviour where novel constraints apply.\udWe demonstrate our approach on systems of varying complexity, including kinematic data from the ASIMO humanoid robot\udwith 22 degrees of freedom

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