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Learning visuomotor policies in simulation is much safer and cheaper than in
the real world. However, due to discrepancies between the simulated and real
data, simulator-trained policies often fail when transferred to real robots.
One common approach to bridge the visual sim-to-real domain gap is domain
randomization (DR). While previous work mainly evaluates DR for disembodied
tasks, such as pose estimation and object detection, here we systematically
explore visual domain randomization methods and benchmark them on a rich set of
challenging robotic manipulation tasks. In particular, we propose an off-line
proxy task of cube localization to select DR parameters for texture
randomization, lighting randomization, variations of object colors and camera
parameters. Notably, we demonstrate that DR parameters have similar impact on
our off-line proxy task and on-line policies. We, hence, use off-line optimized
DR parameters to train visuomotor policies in simulation and directly apply
such policies to a real robot. Our approach achieves 93% success rate on
average when tested on a diverse set of challenging manipulation tasks.
Moreover, we evaluate the robustness of policies to visual variations in real
scenes and show that our simulator-trained policies outperform policies learned
using real but limited data. Code, simulation environment, real robot datasets
and trained models are available at
https://www.di.ens.fr/willow/research/robust_s2r/.
