Evolution of robotic neurocontrollers with intrinsic noise and their behavior in noisy environments

We report on experiments with robotic neurocontrollers with intrinsic noise evolved for a peg pushing task. The specific controller of the simulated robot is a feed-forward network with noisy weights, i.e, the weight values are perturbed by additive, normal noise. The neurocontrollers are evolved in a noise-free environment, and the best-performing networks are then tested in noisy environments, where peg movement and sensor signals are afflicted by noise. We find that the internal (robotic brain) noise is beneficial in coping with external noise, especially, in the case of noisy sensors.