Perception and human interaction for developmental learning of objects and affordances

Abstract

In this paper we describe a cognitive architecture for humanoids interacting with objects and caregivers in a developmental robotics scenario. The architecture is foundational to the MACSi project: it is designed to support experiments to make a humanoid robot gradually enlarge its repertoire of known objects and skills combining autonomous learning, social guidance and intrinsic motivation. This complex learning process requires the capability to learn affordances first. Here, we present the general framework for achieving these goals, focusing on the elementary action, perception and interaction modules. Preliminary experiments performed on the humanoid robot iCub are also discussed.