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

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons85005

Ilg,  W
Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons83791

Bakir,  GH
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons84787

Giese,  MA
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Zitation

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).


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-0013-DC27-C
Zusammenfassung
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.