Correspondence Mapping Induced State and Action Metrics for Robotic Imitation

Aris Alissandrakis, Chrystopher L. Nehaniv, Kerstin Dautenhahn
2007 IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics)  
This paper addresses the problem of body mapping in robotic imitation where the demonstrator and imitator may not share the same embodiment [degrees of freedom (DOFs), body morphology, constraints, affordances, and so on]. Body mappings are formalized using a unified (linear) approach via correspondence matrices, which allow one to capture partial, mirror symmetric, one-to-one, one-to-many, many-to-one, and many-to-many associations between various DOFs across dissimilar embodiments. We show
more » ... diments. We show how metrics for matching state and action aspects of behavior can be mathematically determined by such correspondence mappings, which may serve to guide a robotic imitator. The approach is illustrated and validated in a number of simulated 3-D robotic examples, using agents described by simple kinematic models and different types of correspondence mappings. Index Terms-Correspondence problem, imitation and social learning, programming by demonstration, state and action metrics.
doi:10.1109/tsmcb.2006.886947 pmid:17416158 fatcat:n4cqqfv4cnfldex2lkskfwajim