  Robots have to face challenging perceptual settings, including changes in
viewpoint, lighting, and background. Current simulated reinforcement learning
(RL) benchmarks such as DM Control provide visual input without such
complexity, which limits the transfer of well-performing methods to the real
world. In this paper, we extend DM Control with three kinds of visual
distractions (variations in background, color, and camera pose) to produce a
new challenging benchmark for vision-based control, and we analyze state of the
art RL algorithms in these settings. Our experiments show that current RL
methods for vision-based control perform poorly under distractions, and that
their performance decreases with increasing distraction complexity, showing
that new methods are needed to cope with the visual complexities of the real
world. We also find that combinations of multiple distraction types are more
difficult than a mere combination of their individual effects.
