Simulation

Domain Randomization

Domain Randomization is a sim-to-real technique that randomizes simulator parameters, such as textures, lighting, camera poses, object properties, and physics coefficients, during training so the learned policy or perception model treats reality as one more variation. Popularized by Tobin et al. in 2017 for vision transfer, and extended by automatic domain randomization in OpenAI's dexterous manipulation work, it trades peak in-domain performance for robustness across the randomized range.

Why it matters for physical AI

Randomization remains the most reliable recipe for transferring simulation-trained locomotion and manipulation policies to hardware without real-world fine-tuning, and calibrating its ranges is a standing engineering craft in sim-to-real pipelines.