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• W2081203294 abstract "This paper develops a general policy for learning the relevant features of an imitation task. We restrict our study to imitation of manipulative tasks or 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." @default.
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• W2081203294 date "2004-06-01" @default.
• W2081203294 modified "2023-10-10" @default.
• W2081203294 title "Discovering optimal imitation strategies" @default.
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• W2081203294 doi "https://doi.org/10.1016/j.robot.2004.03.002" @default.
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