Simulation

GAN Domain Adaptation

GAN domain adaptation is the use of generative adversarial networks to translate data between visual domains, most commonly rendering simulated images photorealistic or mapping real images into a canonical simulation-like appearance so that policies trained in one domain transfer to another. Notable robotics examples include GraspGAN for sim-to-real grasping and RCAN, which translated real images to a canonical sim domain for QT-Opt-style grasping, and RetinaGAN for detection-consistent translation.

Why it matters for physical AI

The visual gap between simulation and reality is a principal obstacle to cheap simulated training; learned image translation offered an alternative to domain randomization and still informs modern sim-to-real pipelines.