  This paper shows that self-supervised visual pre-training from real-world
images is effective for learning motor control tasks from pixels. We first
train the visual representations by masked modeling of natural images. We then
freeze the visual encoder and train neural network controllers on top with
reinforcement learning. We do not perform any task-specific fine-tuning of the
encoder; the same visual representations are used for all motor control tasks.
To the best of our knowledge, this is the first self-supervised model to
exploit real-world images at scale for motor control. To accelerate progress in
learning from pixels, we contribute a benchmark suite of hand-designed tasks
varying in movements, scenes, and robots. Without relying on labels,
state-estimation, or expert demonstrations, we consistently outperform
supervised encoders by up to 80% absolute success rate, sometimes even matching
the oracle state performance. We also find that in-the-wild images, e.g., from
YouTube or Egocentric videos, lead to better visual representations for various
manipulation tasks than ImageNet images.
