  Robots act in their environment through sequences of continuous motor
commands. Because of the dimensionality of the motor space, as well as the
infinite possible combinations of successive motor commands, agents need
compact representations that capture the structure of the resulting
displacements. In the case of an autonomous agent with no a priori knowledge
about its sensorimotor apparatus, this compression has to be learned. We
propose to use Recurrent Neural Networks to encode motor sequences into a
compact representation, which is used to predict the consequence of motor
sequences in term of sensory changes. We show that sensory prediction can
successfully guide the compression of motor sequences into representations that
are organized topologically in term of spatial displacement.
