  Modeling the world can benefit robot learning by providing a rich training
signal for shaping an agent's latent state space. However, learning world
models in unconstrained environments over high-dimensional observation spaces
such as images is challenging. One source of difficulty is the presence of
irrelevant but hard-to-model background distractions, and unimportant visual
details of task-relevant entities. We address this issue by learning a
recurrent latent dynamics model which contrastively predicts the next
observation. This simple model leads to surprisingly robust robotic control
even with simultaneous camera, background, and color distractions. We
outperform alternatives such as bisimulation methods which impose
state-similarity measures derived from divergence in future reward or future
optimal actions. We obtain state-of-the-art results on the Distracting Control
Suite, a challenging benchmark for pixel-based robotic control.
