Abstract. We present a system that automatically selects and parameterizes a vision based obstacle avoidance method adapted to a given visual context. This system uses genetic programming and a robotic simulation to evaluate the candidate algorithms. As the number of evaluations is restricted, we introduce a novel method using imitation to guide the evolution toward promising solutions. We show that for this problem, our two-phase evolution process performs better than other techniques. 1

We present a genetic programming system to evolve vision based obstacle avoidance algorithms. In order to develop autonomous behavior in a mobile robot, our purpose is to designautomaticallyanobstacleavoidancecontroller adaptedtothecurrentcontext. Wefirstrecordshortsequenceswherewemanuallyguidethe robottomoveaway fromthewalls. Thissetofrecordedvideoimagesandcommandsisourlearningbase. Geneticprogrammingisused asasupervisedlearningsystemtogeneratealgorithmsthat exhibitthiscorridorcenteringbehavior. Weshowthatthe generatedalgorithmsareefficientinthecorridorthatwas usedtobuildthelearningbase,andthattheygeneralizeto someextentwhentherobotisplacedinavisuallydifferent corridor. More, theevolutionprocesshasproducedalgorithms thatgopastalimitationofoursystem,thatisthe lackofadequateedgeextractionprimitives. This is a good indication of the ability of this method to find efficient solutions for different kinds of environments.