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Policy adaptation with tactile feedback

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3 Author(s)
Argall, B.D. ; Learning Algorithms & Syst. Lab. (LASA, Ecole Polytech. Fed. de Lausanne (EPFL), Lausanne, Switzerland ; Sauser, E.L. ; Billard, A.G.

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.

Published in:

Human-Robot Interaction (HRI), 2011 6th ACM/IEEE International Conference on

Date of Conference:

8-11 March 2011

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