Skip to Main content Skip to Navigation
Conference papers

Sensorimotor learning in a Bayesian computational model of speech communication

Abstract : Although sensorimotor exploration is a basic process within child development, clear views on the underlying computational processes remain challenging. We propose to compare eight algorithms for sensorimotor exploration, based on three components: " accommodation " performing a compromise between goal babbling and social guidance by a master, " local extrapolation " simulating local exploration of the sensorimotor space to achieve motor generalizations and " idiosyncratic babbling " which favors already explored motor commands when they are efficient. We will show that a mix of these three components offers a good compromise enabling efficient learning while reducing exploration as much as possible.
Document type :
Conference papers
Complete list of metadata

Cited literature [19 references]  Display  Hide  Download
Contributor : Marie-Lou Barnaud <>
Submitted on : Tuesday, September 27, 2016 - 2:02:48 PM
Last modification on : Tuesday, May 11, 2021 - 11:37:33 AM
Long-term archiving on: : Wednesday, December 28, 2016 - 12:29:40 PM


Files produced by the author(s)


  • HAL Id : hal-01371719, version 1


Marie-Lou Barnaud, Jean-Luc Schwartz, Julien Diard, Pierre Bessière. Sensorimotor learning in a Bayesian computational model of speech communication. The Sixth Joint IEEE International Conference Developmental Learning and Epigenetic Robotics (ICDL-EPIROB 2016), Sep 2016, Cergy-Pontoise, France. ⟨hal-01371719⟩



Record views


Files downloads