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  On the representation, learning and transfer of spatio-temporal movement characteristics

Ilg, W., Bakir, G., Mezger, J., & Giese, M. (2004). On the representation, learning and transfer of spatio-temporal movement characteristics. International Journal of Humanoid Robotics, 1(4), 613-636. doi:10.1142/S0219843604000320.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-D73D-6 Version Permalink: http://hdl.handle.net/21.11116/0000-0005-48E4-9
Genre: Journal Article

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Ilg, W, Author              
Bakir, GH1, 2, Author              
Mezger, J, Author
Giese, M, 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 modeling 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 modeling and synthesis of complex sequences of human movements that contain movement elements with a 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: 2004-12
 Publication Status: Published in print
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 Table of Contents: -
 Rev. Method: -
 Identifiers: BibTex Citekey: 3173
DOI: 10.1142/S0219843604000320
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Title: International Journal of Humanoid Robotics
Source Genre: Journal
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Pages: - Volume / Issue: 1 (4) Sequence Number: - Start / End Page: 613 - 636 Identifier: ISSN: 0219-8436