On the Representation, Learning and Transfer of Spatio-Temporal Movement Characteristics
2003
Conference Paper
ei
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): | Empirische Inferenz |
Bibtex Type: | Conference Paper (inproceedings) |
Event Name: | Humanoids Proceedings |
Digital: | 0 |
Note: | electronical version |
Organization: | Max-Planck-Gesellschaft |
School: | Biologische Kybernetik |
Links: |
PDF
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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}, doi = {}, month_numeric = {7} } |