Synchrony and perception in robotic imitation across embodiments

Social robotics opens up the possibility of individualized social intelligence in member robots of a community, and allows us to harness not only individual learning by the individual robot, but also the acquisition of new skills by observing other members of the community (robot, human, or virtual). We describe ALICE (Action Learning for Imitation via Correspondences between Embodiments), an implemented generic mechanism for solving the correspondence problem between differently embodied robots. ALICE enables a robotic agent to learn a behavioral repertoire suitable to performing a task by observing a model agent, possibly having a different type of body, joints, different number of degrees of freedom, etc. Previously we demonstrated that the character of imitation achieved will depend on the granularity of subgoal matching, and on the metrics used to evaluate success