This paper describes results from a specialised piece of visuo-robotic equipment which allows the artificial evolution of control systems for visually guided autonomous agents acting in the real world. Preliminary experiments with the equipment are described in which dynamical recurrent networks and visual sampling morphologies are concurrently evolved to allow agents to robustly perform simple visually guided tasks. Some of these control systems are shown to exhibit a surprising degree of adaptiveness when tested against generalised versions of the task for which they were evolved
