Yup! There are a ton of examples of this in robot learning. For instance: https://arxiv.org/abs/1809.10790 by NVIDIA uses photorealistic images and something we call domain randomization to efficiently bridge the sim2real gap for robot grasping.

A core issue seems to be that the better the simulation, the harder it is to generate (compute wise) but simple tweaks to the generative process also go a long way.