From primitive behaviors to goal-directed behavior using affordances

Abstract

In this paper, we studied how a mobile robot equipped with a 3D laser scanner can start from primitive behaviors and learn to use them to achieve goal-directed behaviors. For this purpose, we propose a learning scheme that is based on the concept of "affordances", where the robot first learns about the different kind of effects it can create in the environment and then links these effects with the perception of the initial environment and the executed primitive behavior. It uses these learned relations to create certain effects in the environment and achieve more complex behaviors.