A Genetic Programming System Learning Obstacle Avoiding Behavior and Controlling a Miniature Robot in Real Time

One of the most general forms of representing and specifying behavior is by using a computer language. We have evaluated the use of the evolutionary technique of Genetic Programming (GP) to directly control a miniature robot. The goal of the GP-system was to evolve real-time obstacle avoiding behavior from sensorial. The evolved programs are used in a sense-think-act context. We employed a novel technique to enable real time learning with a real robot using genetic programming. To our knowledge, this is the rst use of GP with a real robot. The method uses a probabilistic sampling of the environment where each individual is tested on a new real-time tness case in a tournament selection procedure. The robots behavior is evolved without any knowledge of the task except for the feedback from a tness function. The tness has a pain and a pleasure part. The negative part of tness, the pain, is simply the sum of the proximity sensor values. In order to keep the robot from standing still or gyrating, it has a pleasure component to its tness. It gets pleasure from going straight and fast. The evolved algorithm shows robust performance even if the robot is lifted and placed in a completely diierent environment or if obstacles are moved around.