  The development of autonomous robotic systems that can learn from human
demonstrations to imitate a desired behavior - rather than being manually
programmed - has huge technological potential. One major challenge in imitation
learning is the correspondence problem: how to establish corresponding states
and actions between expert and learner, when the embodiments of the agents are
different (morphology, dynamics, degrees of freedom, etc.). Many existing
approaches in imitation learning circumvent the correspondence problem, for
example, kinesthetic teaching or teleoperation, which are performed on the
robot. In this work we explicitly address the correspondence problem by
introducing a distance measure between dissimilar embodiments. This measure is
then used as a loss function for static pose imitation and as a feedback signal
within a model-free deep reinforcement learning framework for dynamic movement
imitation between two anthropomorphic robotic arms in simulation. We find that
the measure is well suited for describing the similarity between embodiments
and for learning imitation policies by distance minimization.
