  We propose position-velocity encoders (PVEs) which learn---without
supervision---to encode images to positions and velocities of task-relevant
objects. PVEs encode a single image into a low-dimensional position state and
compute the velocity state from finite differences in position. In contrast to
autoencoders, position-velocity encoders are not trained by image
reconstruction, but by making the position-velocity representation consistent
with priors about interacting with the physical world. We applied PVEs to
several simulated control tasks from pixels and achieved promising preliminary
results.
