  How much does having visual priors about the world (e.g. the fact that the
world is 3D) assist in learning to perform downstream motor tasks (e.g.
navigating a complex environment)? What are the consequences of not utilizing
such visual priors in learning? We study these questions by integrating a
generic perceptual skill set (a distance estimator, an edge detector, etc.)
within a reinforcement learning framework (see Fig. 1). This skill set
("mid-level vision") provides the policy with a more processed state of the
world compared to raw images.
  Our large-scale study demonstrates that using mid-level vision results in
policies that learn faster, generalize better, and achieve higher final
performance, when compared to learning from scratch and/or using
state-of-the-art visual and non-visual representation learning methods. We show
that conventional computer vision objectives are particularly effective in this
regard and can be conveniently integrated into reinforcement learning
frameworks. Finally, we found that no single visual representation was
universally useful for all downstream tasks, hence we computationally derive a
task-agnostic set of representations optimized to support arbitrary downstream
tasks.
