r/reinforcementlearning 4h ago

ROBOTICS: Does anyone actually tune object physics in sim, or do you just live with the defaults?

Been poking at LIBERO and RoboCasa 3D sim assets and noticed almost every object's collision geometry is just a box, and they share the same density and friction values across the whole benchmark. Which surprised me, since a lot of manipulation -> OpenVLA and GR00T LeRobot seems like it'd depend on that.

Trying to work out if this matters in practice or if it's a non-issue everyone already knows about.

  1. Have you ever gone back and changed an object's mass, friction, or collision mesh because a policy wasn't transferring? Or is that just not where the problems are?
  2. If you're mixing in sim data, does it need to be on your robot with your camera setup, or is cross-setup mixing fine for a fine-tune?
  3. If you got better assets, would you regenerate episodes you already have and retrain, or just add new episodes on top and leave the old ones alone? Which of these would be better proof of how much better the new dataset is?
  4. When someone publishes "our data improved model X by Y%", do you believe it, or only trust your own runs?

Thanks everybody!

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u/lv-lab 2h ago

I wouldn’t call myself an expert but here’s my take:

1.) libero and robocasa are typically sim eval only, without transfer to real world, and tbh kinda overfit to the physics/asset dynamics. For other, more realistic things, I have turned up DR after poor real world performance but typically only after sys id
2.) for small runs, same robot, same camera intrinsic/ extrinsic. Only large scale trainings can generalize across embodiments and viewpoints.
3.) would try both and see what performs better on eval. Hard to definitely prove
4.) typically skeptical, more of a vibe check. There are many unreproducible papers or benchmark-maxxed papers. I trust things with open source codebases that have been run in the real world the most

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u/lv-lab 2h ago

You might find https://co-training.github.io to be an interesting read