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Synthetic data works better for robots since you can generate endless scenarios based on real physical laws.

Do you know of success stories here? Success of transferring models learned in physics simulation to the real world.

When we (ZenRobotics) tried this 15 years ago a big problem was the creation of sufficiently high-fidelity simulated worlds. Gathering statistics and modelling the geometry, brittleness, flexibility, surface texture, friction, variable density etc of a sufficiently large variety of objects was harder than gathering data from the real world.

We have massively better physics simulations today than 15 years ago, so the limitations you found back then don't apply today. It might still not be enough, but 15 years is such a long time with Moore's law and we already know all the physics so we just needed more computation to do what is needed.

Example of modern physics simulation: https://www.youtube.com/watch?v=7NF3CdXkm68

Google has done training in simulation: https://x.company/projects/everyday-robots/#:~:text=other%20...

I believe this is the most popular tool now: https://github.com/google-deepmind/mujoco

Thanks for the links.

AFAICT these have not resulted in any shipping products.

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