|Reference Type||Journal Article|
|Author(s)||Billard, A.;Epars, Y.;Calinon, S.;Cheng, G.;Schaal, S.|
|Title||Discovering optimal imitation strategies|
|Journal/Conference/Book Title||Robotics and Autonomous Systems|
|Keywords||imitation, intent extraction, motor control|
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.
|Short Title||Discovering optimal imitation strategies|
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