Domain Randomization

A sim-to-real technique that trains policies in simulation with heavily randomized environment parameters (lighting, textures, physics properties, camera poses, object shapes) so the policy learns to be robust to the variations it will encounter in the real world. If the randomization distribution is wide enough to encompass reality, the policy transfers without any real-world fine-tuning.