A model-free, case-based learning and control algorithm called S-learning is described as implemented in a simulation of a light-seeking mobile robot. S-learning demonstrated learning of robotic and environmental structure sufficient to allow it to achieve its goal (reaching a light source). No modeling information about the task or calibration information about the robot's actuators and sensors were used in S-learning's planning. The ability of S-learning to make movement plans was completely dependent on experience it gained as it explored. Initially it had no experience and was forced to wander randomly. With increasing exposure to the task, S-learning achieved its goal with more nearly optimal paths. The fact that this approach is model-free and case-based implies that it may be applied to many other systems, perhaps even to systems of much greater complexity.
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