<title>General visual robot controller networks via artificial evolution</title>

Abstract

We discuss recent results from our ongoing research concerning the application of artificial evolution techniques (i.e., an extended form of genetic algorithm) to the problem of developing `neural' network controllers for visually guided robots. The robot is a small autonomous vehicle with extremely low-resolution vision, employing visual sensors which could readily be constructed from discrete analog components. In addition to visual sensing, the robot is equipped with a small number of mechanical tactile sensors. Activity from the sensors is fed to a recurrent dynamical artificial `neural' network, which acts as the robot controller, providing signals to motors governing the robot's motion. Prior to presentation of new results, this paper summarizes our rationale and past work, which has demonstrated that visually guided control networks can arise without any explicit specification that visual processing should be employed: the evolutionary process opportunistically makes use of visual information if it is available.