Learning from Demonstration and Correction via Multiple Modalities for a Humanoid Robot
Brenna Argall* and Aude Billard*
Learning Algorithms and Systems Laboratory (LASA), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
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This paper reports ongoing work that employs multiple demonstration modalities in order to accomplish motion control learning in a multi-staged policy adaptation process. A novel interface for providing tactile guidance to correct learned motion control behaviors is introduced. This interface extends our prior work by making use of a more sophisticated set of tactile sensors, developed by the ROBOSKIN consortium.
© Owned by the authors, published by EDP Sciences, 2011