The real vs synthetic robot training data debate is one of the most important strategic decisions for any embodied AI team building a training pipeline.
When to use real-world capture, when to use simulation, and when a hybrid blend outperforms either alone. A practical comparison for embodied AI teams.
The real-versus-synthetic debate in robot training data is often framed as a binary choice. In practice, the question is not which is better in the abstract — it is which is right for your specific policy, at your specific stage of development, for your specific task distribution.
Real-world capture produces data with genuine physical fidelity — the noise, variability, and edge cases that the robot will encounter in deployment. It is the gold standard for fine-tuning and for tasks where physical dynamics are critical. The trade-off is cost: high-quality real-world data is expensive and slow to generate. It is also difficult to generate adversarial or rare scenarios systematically.
Simulation produces data cheaply and at unlimited scale. It is ideal for initial policy exploration, for generating adversarial edge cases, and for tasks where the sim-to-real gap is manageable. The risk is overfitting to simulation dynamics that do not transfer — a risk that needs to be measured explicitly and managed through domain randomisation and transfer benchmarking.
Most production-quality policies are trained on a blend: a synthetic foundation dataset supplemented by real-world demonstrations for fine-tuning and transfer. The optimal blend ratio depends on the task and the sim-to-real gap — which Roborax measures explicitly in every program that includes a synthetic component.
Related: Sim-to-real data — Data services.
The right real-versus-synthetic blend ratio for a robot training data program cannot be determined theoretically — it needs to be measured empirically through transfer benchmarking. Roborax’s approach for programs with a synthetic component is to establish a real-world transfer benchmark first, generate an initial synthetic dataset, measure the transfer delta, and iterate on the domain randomisation parameters until the gap is within your target range. Only then is the synthetic dataset used at scale. Related: Sim-to-real data services — Evaluation benchmarks.
