Do as I Do: Correspondences across Different Robotic Embodiments

Abstract

Behaviour matching and imitation serve as fundamental mechanisms for social learning, the development of social skills, and the evolution of cultures. Imitation and observational learning as means for acquiring new behaviours also represent a largely untapped resource for robotics and artificial life - both in the study of “life as it could be” and for applications of biological mechanisms to synthetic worlds. A crucial problem in imitation is the correspondence problem, mapping action sequences of the demonstrator and the imitator agent. This problem becomes particularly obvious when the two agents do not share the same embodiment and affordances. This paper describes work-in-progress using the general imitating mechanism ALICE (Action Learning for Imitation via Correspondences between Embodiments), trying to find solutions for the correspondence problem between different configurations of robotic arms.