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

2003

Conference Paper

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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.

Author(s): Ilg, W. and Bakir, GH. and Mezger, J. and Giese, MA.
Journal: Humanoids Proceedings
Pages: 0-0
Year: 2003
Month: July
Day: 0

Department(s): Empirical Inference
Bibtex Type: Conference Paper (inproceedings)

Event Name: Humanoids Proceedings

Digital: 0
Note: electronical version
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: PDF

BibTex

@inproceedings{2295,
  title = {On the Representation, Learning and Transfer of Spatio-Temporal Movement Characteristics},
  author = {Ilg, W. and Bakir, GH. and Mezger, J. and Giese, MA.},
  journal = {Humanoids Proceedings},
  pages = {0-0},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  month = jul,
  year = {2003},
  note = {electronical version},
  month_numeric = {7}
}