Learning structural affordances through self-exploration

The goal of this paper is to develop a cognitive developmental approach for a humanoid robot so that it can provisionally discover self-affordance relations between certain arm limb movements and corresponding motor units by exploring the outcomes of its random arm movements while in a crawling position. Learning of the right and the left arm affordances is based on self-exploration and a set of experience similarly to how a human baby discovers action-effect relations of own arm movements. We address the early development of self-affordances, similar to infants, which encodes the relationships between actions, objects, and the effect on the environment.