Learning robot behaviors by evolving genetic programs

Abstract

A method for evolving behavior-based robot controllers using genetic programming is presented. Due to their hierarchical nature, genetic programs are useful representing high-level knowledge for robot controllers. One drawback is the difficulty of incorporating sensory inputs. To overcome the gap between symbolic representation and direct sensor values, the elements of the function set in genetic programming is implemented as a single-layer perceptron. Each perceptron is composed of sensory input nodes and a decision output node. The robot learns proper behavior rules based on local, limited sensory information without using an internal map. First, it learns how to discriminate the target using single-layer perceptrons. Then, the learned perceptrons are applied to the function nodes of the genetic program tree which represents a robot controller. Experiments have been performed using Khepera robots. The presented method successfully evolved high-level genetic programs that control the robot to find the light source from sensory inputs.