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| Record Number | 1959 |
| Reference Type | Journal Article |
| Author(s) | Billard, A.;Epars, Y.;Calinon, S.;Cheng, G.;Schaal, S. |
| Year | 2004 |
| Title | Discovering optimal imitation strategies |
| Journal/Conference/Book Title | Robotics and Autonomous Systems |
| Keywords | imitation, intent extraction, motor control |
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.
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| Notes | clmc |
| Volume | 47 |
| Number | 2-3 |
| Pages | 68-77 |
| Short Title | Discovering optimal imitation strategies |
| Papers are available as Adobe PDF ".pdf" files. Adobe Reader is available for free for all computer platforms.
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