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Conference papers

Auto-supervised learning in the Bayesian Programming Framework

Pierre Dangauthier 1 Pierre Bessiere 1 Anne Spalanzani 1 
1 E-MOTION - Geometry and Probability for Motion and Action
GRAVIR - IMAG - Laboratoire d'informatique GRAphique, VIsion et Robotique de Grenoble, Inria Grenoble - Rhône-Alpes
Abstract : Domestic and real world robotics requires continuous learning of new skills and behaviors to interact with humans. Auto-supervised learning, a compromise between supervised and completely unsupervised learning, consist in relying on previous knowledge to acquire new skills. We propose here to realize auto-supervised learning by exploiting statistical regularities in the sensorimotor space of a robot. In our context, it corresponds to achieve feature selection in a Bayesian programming framework. We compare several feature selection algorithms and validate them on a real robotic experiment.
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Contributor : Pierre Bessiere Connect in order to contact the contributor
Submitted on : Tuesday, March 14, 2006 - 10:47:44 AM
Last modification on : Wednesday, February 2, 2022 - 3:58:22 PM
Long-term archiving on: : Saturday, April 3, 2010 - 8:21:01 PM


  • HAL Id : hal-00019663, version 1



Pierre Dangauthier, Pierre Bessiere, Anne Spalanzani. Auto-supervised learning in the Bayesian Programming Framework. 2005, pp.1-6. ⟨hal-00019663⟩



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