  In this work, we explore self-supervised visual pre-training on images from
diverse, in-the-wild videos for real-world robotic tasks. Like prior work, our
visual representations are pre-trained via a masked autoencoder (MAE), frozen,
and then passed into a learnable control module. Unlike prior work, we show
that the pre-trained representations are effective across a range of real-world
robotic tasks and embodiments. We find that our encoder consistently
outperforms CLIP (up to 75%), supervised ImageNet pre-training (up to 81%), and
training from scratch (up to 81%). Finally, we train a 307M parameter vision
transformer on a massive collection of 4.5M images from the Internet and
egocentric videos, and demonstrate clearly the benefits of scaling visual
pre-training for robot learning.
