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Discovering optimal imitation strategies
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.
@article{Billard_RAS_2004, title = {Discovering optimal imitation strategies}, booktitle = {Robotics and Autonomous Systems}, abstract = {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.}, volume = {47}, number = {2-3}, pages = {68-77}, year = {2004}, note = {clmc}, slug = {billard_ras_2004}, author = {Billard, A. and Epars, Y. and Calinon, S. and Cheng, G. and Schaal, S.}, crossref = {p1959} }