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Behavior adaptation with execution experience is a practical feature for any policy learning system. Our work provides performance feedback to a robot learner in the form of tactile corrections from a human teacher, for the purpose of policy refinement as well as policy reuse. Multiple variants of our general approach have been validated on the iCub robot, as building blocks towards a high-DoF humanoid system that integrates tactile sensing on the hands and arms into complex behaviors and sophisticated learning routines.
Date of Conference: 8-11 March 2011