Neural mechanisms for training autonomous robots

Minimalist neural mechanisms are suitable tools for programming and training autonomous robots. This paper explores the limitations of hand-crafted minimalist robot control mechanisms based on a neural paradigm, and then shows that these mechanisms are well suited to robot training using well understood neural learning mechanisms. Training a robot is more powerful than other methods more commonly used for robot learning (such as reinforcement learning and genetic techniques). A trained robot is told more than whether it was wrong or right for a particular action or sequence (reinforcement learning), the robot is also told what it should have done (supervised learning). Robots can hence develop appropriate behaviour much more rapidly. The neural mechanisms and training techniques have been developed on a kinematically realistic simulator. The mechanisms have been ported from simulated vehicles to a real vision guided robot: CORGI. Results from the simulation and CORGI are presented.