Autonomous exploration: driven by uncertainty

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Abstract
Passively accepting measurements of the world is not enough, as the data we obtain is always incomplete, and the inferences made from it are 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 fidelity of its internal representations, and keeps searching until those representations are acceptable. We call this paradigm autonomous exploration, and the machine an a autonomous explorer. This paper has two major contributions. The first is a theory that tells us how to explore, and which confirms the intuitive ideas we have put forward previously. The second is an implementation of that theory. The system is entirely bottom-up and does not depend on a priori knowledge of the environment. To our knowledge it is the first to have successfully closed the loop between gaze planning and the inference of complex 3D models.

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