Billard, A., Epars, Y., Calinon, S., Cheng, G. and Schaal, S. (2004)
Discovering Optimal Imitation Strategies
Robotics and Autonomous Systems, Special Issue: Robot Learning from Demonstration, 47:2-3, 69-77.


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.

Bibtex reference

  author = "Billard, A. and Epars, Y. and Calinon, S. and Cheng, G. and Schaal, S.",
  title = "Discovering Optimal Imitation Strategies",
  journal = "Robotics and Autonomous Systems",
  year = "2004",
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