Autonomous Exploration: Driven by Uncertainty Autonomous Exploration: Driven by Uncertainty

Passively accepting measurements of the world is not enough, as the data we obtain is always incomplete, and the inferences made from it uncertain to a degree which is often unacceptable. If we are to build machines that operate autonomously they will always be faced with this dilemma, and can only be successful if they play a much more active role. This paper presents such a machine. It deliberately seeks out those parts of the world which maximize the delity of its internal representations, and keeps searching until those representations are acceptable. We call this paradigm autonomous exploration, and the machine an autonomous explorer. This paper has two major contributions. The rst is a theory that tells us how to explore, and which connrms the intuitive ideas we have put forward previously. The second is an implementation of that theory. In our laboratory we have constructed a working autonomous explorer and here for the rst time can show it in action. The system is entirely bottom-up and does not depend on any a priori knowledge of the environment. To our knowledge it is the rst to have successfully closed the loop between gaze planning and the inference of complex 3D models. A notre connaissance, c'est la premi ere fois que la boucle de la planiication du regard et de l'inf erence de mod eles complexes tridimensionnels est compl^ et ee avec succ es.