Glossary · AI & ML
Domain randomization
In brief
Domain randomization is the technique of training a policy across many randomized variants of the simulator — different physics parameters, lighting, textures, sensor noise — so the policy learns to be robust to the variation. It is the standard tool for closing the sim-to-real gap.
A policy trained in a single perfectly-tuned simulator overfits to that simulator. Run the same policy on a real robot and it breaks: real friction is different, real latency is different, real sensor noise has a different distribution. Domain randomization defends against this by varying the simulator parameters during training and forcing the policy to generalize.
How much randomization is enough is project-specific. Too little and sim-to-real fails; too much and the policy becomes overly conservative or just doesn't learn. The art is in picking the variation distributions to match real-world uncertainty.
Related terms
See it in the wild
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Glossary entries are upstream. The catalog is where the implementations live.