Discriminative and Adaptive Imitation in Uni-Manual and Bi-Manual Tasks

This paper addresses the problems of what to imitate and how to imitate in simple uni- and bi-manual manipulatory tasks. To solve the what to imitate issue, we use a probabilistic method, based on Hidden Markov Models, for extracting the relative importance of reproducing either the gesture or the specific hand path in a given task. This allows us to determine a metric of imitation performance. To solve the how to imitate issue, we compute the trajectory that optimizes the metric, given a set of robot's body constraints. We validate the methods in a series of experiments, where a human demonstrator teaches through kinesthetic a humanoid robot how to manipulate simple objects.


Published in:
Robotics and Autonomous Systems, 54, 5, 370-384
Year:
2006
Keywords:
Other identifiers:
DAR: 8674
Laboratories:




 Record created 2007-04-15, last modified 2018-12-03

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