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Unsupervised learning of sensory-motor primitives

Todorov, E; Ghahramani, Z; (2003) Unsupervised learning of sensory-motor primitives. In: (pp. pp. 1750-1753). IEEE Computer Society: Piscataway, US.

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The search for motor primitives has captured the attention of researches in both biological and computational motor control. Yet a theory of how to construct such primitives from first principles is lacking. Here we propose to do that by building a compact forward model of the sensory-motor periphery via unsupervised learning. We also propose a method for probabilistic inversion of the forward model, which yields low-level feedback loops that can simplify control. The idea is applied to simulated biomechanical systems of varying levels of detail.

Type: Proceedings paper
Title: Unsupervised learning of sensory-motor primitives
ISBN: 0780377893
DOI: 10.1109/IEMBS.2003.1279744
Publisher version: http://dx.doi.org/10.1109/IEMBS.2003.1279744
UCL classification: UCL > School of BEAMS
UCL > School of BEAMS > Faculty of Engineering Science
URI: http://discovery.ucl.ac.uk/id/eprint/185398
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