  This paper discusses various techniques to let an agent learn how to predict
the effects of its own actions on its sensor data autonomously, and their
usefulness to apply them to visual sensors. An Extreme Learning Machine is used
for visuomotor prediction, while various autonomous control techniques that can
aid the prediction process by balancing exploration and exploitation are
discussed and tested in a simple system: a camera moving over a 2D greyscale
image.
