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.
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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⟩

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