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In order to explore and act autonomously in an environment, an agent needs tolearn from the sensorimotor information that is captured while acting. Byextracting the regularities in this sensorimotor stream, it can learn a modelof 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 highdimensional raw observation, which is noisy and ambiguous. In this paper, welearn sensory representations from sensorimotor prediction. We propose a modelwhich integrates sensorimotor information over time, and project it in asensory representation which is useful for prediction. We emphasize on a simpleexample the role of motor and memory for learning sensory representations.

Representation Learning in Partially Observable Environments using Sensorimotor Prediction