Evolving Vision Controllers with a Two-Phase Genetic Programming System Using Imitation

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.