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Reference TypeConference Proceedings
Author(s)Billard, A.;Epars, Y.;Schaal, S.;Cheng, G.
Year2003
TitleDiscovering imitation strategies through categorization of multi-cimensional data
Journal/Conference/Book TitleIEEE International Conference on Intelligent Robots and Systems (IROS 2003)
Keywordsmovement primitives, sequencing
AbstractAn essential problem of imitation is that of determining ”what to imitate”, i.e. to determine which of the many features of the demonstration are relevant to the task and which should be reproduced. The strategy followed by the imitator can be modeled as a hierarchical optimization system, which minimizes the discrepancy between two multidimensional datasets. We consider imitation of a manipulation task. 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 manipulation tasks and controls task reproduction by a full body humanoid robot. or the complete path followed by the demonstrator. We follow a similar taxonomy and apply it to the learning and reproduction of a manipulation task by a humanoid robot. We take the perspective that the features of the movements to imitate are those that appear most frequently, i.e. the invariants in time. The model builds upon previous work [3], [4] and is composed of a hierarchical time delay neural network that extracts invariant features from a manipulation task performed by a human demonstrator. The system analyzes the Carthesian trajectories of the objects and the joint
Place PublishedLas Vegas, NV, Oct. 27-31
Short TitleDiscovering imitation strategies through categorization of multi-cimensional data
URL(s) http://www-clmc.usc.edu/publications/B/billard-IROS2003.pdf

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