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  On the Representation, Learning and Transfer of Spatio-Temporal Movement Characteristics

Ilg, W., Bakir, G., Mezger, J., & Giese, M. (2003). On the Representation, Learning and Transfer of Spatio-Temporal Movement Characteristics. In Humanoids Proceedings (pp. 0-0).

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-DC27-C Version Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-DC28-A
Genre: Conference Paper

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Ilg, W1, Author              
Bakir, GH2, Author              
Mezger, J, Author
Giese, MA1, Author              
Affiliations:
1Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497794              
2Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              

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 Abstract: In this paper we present a learning-based approach for the modelling of complex movement sequences. Based on the method of Spatio-Temporal Morphable Models (STMMS. We derive a hierarchical algorithm that, in a first step, identifies automatically movement elements in movement sequences based on a coarse spatio-temporal description, and in a second step models these movement primitives by approximation through linear combinations of learned example movement trajectories. We describe the different steps of the algorithm and show how it can be applied for modelling and synthesis of complex sequences of human movements that contain movement elements with variable style. The proposed method is demonstrated on different applications of movement representation relevant for imitation learning of movement styles in humanoid robotics.

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 Dates: 2003-07
 Publication Status: Published in print
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 Identifiers: BibTex Citekey: 2295
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Title: Humanoids Proceedings
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Title: Humanoids Proceedings
Source Genre: Proceedings
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Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 0 - 0 Identifier: -