Learning new representations and goals for autonomous robots

Most robotic systems are designed for given goals. Even learning systems follow this paradigm by trying to improve overall performance for a given task. These systems are limited by the knowledge of the developers and are not able to overpass their initial set of goals. We propose to explore a new kind of sensory motor systems that are able to acquire new representations and new goals starting from an initial small set. Instead of developing algorithms for a given task, we want to develop a general approach that is task acquisition oriented. The work reported in this paper is just a beginning and propose a theoretical framework and first results for such systems.