  Affordances are fundamental descriptors of relationships between actions,
objects and effects. They provide the means whereby a robot can predict
effects, recognize actions, select objects and plan its behavior according to
desired goals. This paper approaches the problem of an embodied agent exploring
the world and learning these affordances autonomously from its sensory
experiences. Models exist for learning the structure and the parameters of a
Bayesian Network encoding this knowledge. Although Bayesian Networks are
capable of dealing with uncertainty and redundancy, previous work considered
complete observability of the discrete sensory data, which may lead to hard
errors in the presence of noise. In this paper we consider a probabilistic
representation of the sensors by Gaussian Mixture Models (GMMs) and explicitly
taking into account the probability distribution contained in each discrete
affordance concept, which can lead to a more correct learning.
