Synthetic Data vs Real-World Data for Robotics
Synthetic data is cheap, perfectly labeled, and infinitely repeatable. Real-world data is expensive, noisy, and exactly what your robot will face. The practical question is never which one — it's the mix, and how you verify the mix is working.
// key_takeaways
- The question is the mix, not either/or.
- Validate synthetic batches against a real-world holdout.
- Report the synthetic/real split as a first-class dataset property.
Synthetic shines where geometry and physics dominate: pose variation, camera angles, rare spatial configurations, hazardous scenarios you can't stage. It struggles with the texture of reality — sensor noise, material appearance, lighting interplay, human unpredictability — which is precisely where many production failures occur.
The discipline that makes the mix work is synthetic-to-real validation: hold out a real-world test set that represents deployment conditions, and measure whether adding synthetic batches moves real-world metrics. If a synthetic class doesn't transfer, it's compute spent training the model on a video game.
Treat the synthetic/real split as a reported property of every dataset — it belongs on the certificate, next to coverage and balance — so training decisions are made with eyes open.