Learning Maps Between Sensorimotor Systems on a Humanoid Robot

The cerebellum has long been known to be associated with the coordination of the human motor system. Contemporary research indicates that this is one product of the cerebellum's true function: the generation of dynamic models of systems both within and without the body. This thesis describes the instantiation of one such model on the humanoid robot Cog, developed at the MIT Artificial Intelligence Laboratory. The model takes the form of an adaptive mapping of head movements into anticipated motion in the visual field. This model is part of a visual subsystem which allows Cog to detect motion in the environment without being confused by movement of its own head. The author hopes that this work will be the first step in creating a generalized system for generating models between sensorimotor systems, and that such a system will be the first step in the development of a fully-functional artificial cerebellum for Cog. Thesis Supervisor: Rodney A. Brooks Title: Professor of Electrical Engineering and Computer Science