Learning to build visual categories from perception-action associations

Abstract

In this paper we describe how a mobile robot can autonomously learn and "recognize" simple objects present somewhere in an indoor visual scene. The experiment involves transposing a classical conditioning experiment on a mobile robot. We propose the use of a selective attention mechanism to reduce the amount of computation involved by the complete image analysis. Objects are categorized according to their associated actions that are learned in accordance with a reward/punishment procedure. Our approach emphasizes the importance of a movement reflex mechanism based on the use of the same egocentric representation from the visual information to the motor output. Finally, we highlight the impact of information coding in self organised topological maps on the robot performances.