Domain randomization

Domain randomization trains a model on deliberately varied simulations rather than one carefully tuned to match reality. By randomizing textures, colors, lighting, camera placement, and physical parameters across training, it exposes the model to such a wide range of conditions that the real world looks like just another variation. The model learns features that hold across all of them.

It is a leading strategy for crossing the sim-to-real gap without perfectly modeling reality.

At each training episode the simulator samples new values for visual and physical parameters, so no two runs look or behave exactly the same. A policy or perception model trained across this distribution cannot rely on any single simulated quirk, so it learns robust, transferable features. The real world, ideally, falls inside the range the model already saw.

Simulation is cheap and safe but never a perfect copy of reality, and domain randomization is a practical way to make that gap matter less. It is a core tool of physical AI, letting teams train policies and perception at scale in simulation and deploy them on real robots.

By training across many randomized conditions, the model generalizes so broadly that real-world conditions look like another variation it can already handle.

Visual factors like textures, colors, and lighting, and physical factors like friction, mass, and sensor noise, depending on the task.

Sim-to-real transfer, Synthetic data, Simulation, Policy.