Forward models enable both robots and humans to predict the sensory consequences of their motor actions. To learn its own forward models a robot needs to experiment with its own motor system, in the same way that human infants need to babble as a part of their motor development. In this paper we investigate how this babbling with the motor system can be influenced by the forward models’ own knowledge of their predictive ability. By spending more time babbling in regions of motor space that require more accuracy in the forward model, the learning time can be reduced. The key to guiding this exploration is the use of probabilistic forward models, which are capable of learning and predicting not just the sensory consequence of a motor command, but also an estimate of how accurate this prediction is. An experiment was carried out to test this theory on a robotic pan tilt camera.