  Real world data, especially in the domain of robotics, is notoriously costly
to collect. One way to circumvent this can be to leverage the power of
simulation to produce large amounts of labelled data. However, training models
on simulated images does not readily transfer to real-world ones. Using domain
adaptation methods to cross this "reality gap" requires a large amount of
unlabelled real-world data, whilst domain randomization alone can waste
modeling power. In this paper, we present Randomized-to-Canonical Adaptation
Networks (RCANs), a novel approach to crossing the visual reality gap that uses
no real-world data. Our method learns to translate randomized rendered images
into their equivalent non-randomized, canonical versions. This in turn allows
for real images to also be translated into canonical sim images. We demonstrate
the effectiveness of this sim-to-real approach by training a vision-based
closed-loop grasping reinforcement learning agent in simulation, and then
transferring it to the real world to attain 70% zero-shot grasp success on
unseen objects, a result that almost doubles the success of learning the same
task directly on domain randomization alone. Additionally, by joint finetuning
in the real-world with only 5,000 real-world grasps, our method achieves 91%,
attaining comparable performance to a state-of-the-art system trained with
580,000 real-world grasps, resulting in a reduction of real-world data by more
than 99%.
