Evolution of Efficient Gait with an Autonomous Biped Robot Using Visual Feedback

In this paper we present the autonomous, walking humanoid robot ELVINA and the first experiments in genetic programming performed with it. A steady state evolutionary strategy is running on the robot’s onboard computer. Individuals are evaluated and fitness scores are automatically determined using the robots onboard digital camera and near-infrared range sensor. The experiments are performed in order to optimize a by hand developed locomotion controller. By using this system, we evolved gait patterns that locomote the robot in a straighter path and in a more robust way, than the previously manually developed gait did.