Predicting Affordances from Gist

This paper presents an incremental learning mechanism to create associations between the affordances provided by the environment and its gist. The proposed model aims at helping the agent on the prioritisation of its perceptual resources, and consequently on visual attention. The focus on affordances, rather than on objects, enables a self-supervised learning mechanism without assuming the existence of symbolic object representations, thus facilitating its integration on a developmental framework. The focus on affordances also contributes to our understanding on the role of sensorimotor coordination on the organisation of adaptive behaviour. Promising results are obtained with a physical experiment on a natural environment, where a camera was handled as if it was being carried by an actual robot performing obstacle avoidance, trail following and wandering behaviours.