A probabilistic modeling of MOSAIC learning

Abstract

Humans can generate accurate and appropriate motor commands in various, and even uncertain, environments. MOSAIC (MOdular Selection And Identification for Control) was originally proposed to describe this human ability, but this model is hard to analyze mathematically because of its emphasis on biological plausibility. In this article, we present an alternative and probabilistic model of MOSAIC (p-MOSAIC) as a mixture of normal distributions and an online EM-based learning method for its predictors and controllers. A theoretical consideration shows that the learning rule of p-MOSAIC corresponds to that of MOSAIC except for some points which are mostly related to the learning of controllers. The results of experiments using synthetic datasets demonstrate some practical advantages of p-MOSAIC. One is that the learning rule of p-MOSAIC stabilizes the estimation of “responsibility.” Another is that p-MOSAIC realizes more accurate control and robust parameter learning in comparison to the original MOSAIC, especially in noisy environments, due to the direct incorporation of the noises into the model.