15–30 minutes of real-world robot data.
That's now enough to go from
sim-to-real failure to working robot.
Let’s see…
You train a robot in simulation. You deploy it in the real world. It fails. The physics don't match. So you try to fine-tune it with real data, but… you never have enough real data, and the fine-tuning breaks everything the simulation taught it.
SimDist fixes this with one key decision: don't transfer the policy. Transfer the world model.
Keep the reward and value knowledge from simulation frozen. Only update the part that's actually wrong, how the robot predicts physics.
Now the robot doesn't have to relearn the entire task in the real world. It already knows what success looks like. It just needs to correct its understanding of how the real world moves.
The part that makes this work:
they also trained on failures and recoveries; not just perfect demonstrations. Without that, the planner finds the gaps and exploits them. With it, the robot can tell a good future from a bad one. That's all it needs.
Results on peg insertion, table leg assembly, locomotion on slippery and uneven surfaces. Tasks that require precision, force, and quick reaction.
Thanks for sharing, Tyler Westenbroek ([@ty_westenbroek].
Interactive visualization + paper: https://t.co/Ns4txW6Apk
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