This paper develops a general policy for learning relevant features of an imitation task. We restrict our study to imitation of manipulative tasks or of gestures. The imitation process is modeled as a hierarchical optimization system, which minimizes the discrepancy between two multi- dimensional datasets. To classify across manipulation strategies, we apply a probabilistic analysis to data in Cartesian and joint spaces. We determine a general metric that optimizes the policy of task reproduction, following strategy determination. The model successfully discovers strategies in six different imitative tasks and controls task reproduction by a full body humanoid robot.
Thibault Lucien Christian Asselborn, Wafa Monia Benkaouar Johal, Thanasis Hadzilacos
Aude Billard, Farshad Khadivar, Konstantinos Chatzilygeroudis
Mohamed Bouri, Amalric Louis Ortlieb, Benoît Walter Denkinger, Peter Lichard, Tommaso Tracchia