Domain Randomization

Domain Randomization
A training technique that varies simulated environments or dynamics to improve robustness when transferring models to reality.
Area	Physical AI and robotics
Applications	Research, engineering and systems operation

Domain Randomization a training technique that varies simulated environments or dynamics to improve robustness when transferring models to reality.

Method

Training examples are generated with variations in appearance or physical parameters such as texture, illumination, camera position, mass or friction. Rather than matching one fixed simulation precisely, the method exposes a learner to multiple possible conditions.

Research example

Tobin and colleagues reported a 2017 study on object localization using randomized simulated imagery, motivated by improving transfer to real-world observations. The study is influential as an early demonstration, not a guarantee that all randomization helps.

Design choices

Randomization must reflect plausible variation without erasing task-relevant information. Excessively broad variation can make a problem needlessly hard, while narrow variation can leave important real-world cases untested.

Relationship to other methods

Domain randomization is one tool within Sim-to-Real Transfer. It can be combined with calibration, real-data adaptation and scenario-based safety evaluation.

See also

• Sim-to-Real Transfer
• Robot Dataset Documentation
• Physical AI Benchmarking

Further reading

• Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World — Tobin et al. (arXiv, 2017)
• AI Risk Management Framework (AI RMF 1.0) — NIST