Unsupervised learning of sensory-motor primitives.
(pp. pp. 1750-1753).
IEEE Computer Society: Piscataway, US.
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.
|Title:||Unsupervised learning of sensory-motor primitives|
|UCL classification:||UCL > School of BEAMS
UCL > School of BEAMS > Faculty of Engineering Science
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