  In order to explore and act autonomously in an environment, an agent needs to
learn from the sensorimotor information that is captured while acting. By
extracting the regularities in this sensorimotor stream, it can learn a model
of the world, which in turn can be used as a basis for action and exploration.
  This requires the acquisition of compact representations from a possibly high
dimensional raw observation, which is noisy and ambiguous. In this paper, we
learn sensory representations from sensorimotor prediction. We propose a model
which integrates sensorimotor information over time, and project it in a
sensory representation which is useful for prediction. We emphasize on a simple
example the role of motor and memory for learning sensory representations.
