A neural system for behavioral conditioning of mobile robots

Abstract

A control system for mobile robots which is reminiscent of the modular distributed architecture of the brain is proposed. This system is motivated by the various neural network models of S. Grossberg (1982, 1988) and M. Seibert and A.M. Waxman (1989). The approach takes the point of view that the robot must be adaptive to its environment and learn from experience. The system utilizes neural networks for learning and performance at all stages, from visual object recognition to behavioral conditioning. The system includes networks for early visual perception, pattern learning and recognition, object associations, emotional states, behavioral actions, and motor control. These networks are interconnected by a variety of adaptive pathways. The system demonstrates self-organizing, teacher-controlled, and reinforcement learning paradigms, and it integrates these into a system in which external events interact with internal emotional states. The system has been implemented on a mobile robot, MAVIN (mobile adaptive visual navigator), and a variety of behavioral conditioning paradigms are demonstrated