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Finally, someone automating the boring infrastructure stuff! Road markings is tough. Nobody cares about them until they're gone. Then suddenly everyone's mad. But someone has to paint those lines, in traffic, in bad weather, repetitively. So 10LINES built a robot that just... does it. Workers don't have to... show more
34,915 次观看 • 2 个月前 •via X (Twitter)
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![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 ([Tyler Westenbroek]. Interactive visualization + paper: ——- Weekly robotics and AI insights. Subscribe free:](https://image.24vids.com/tw-2057007432878928150/ext_tw_video_thumb/2057007419692101632/pu/img/Tr8ccWDNdIoIxpos.jpg)
