I’m also a sucker for a fun website. Check out our interactive demo where you can see some of the environments and learned behaviors. We’ve also open sourced USDZ assets of the sourced environments. (8/N)

Step 5: One neat feature is that in a test environment, human demos aren’t even required. Scan in an environment video to build a test-time simulation and let the generalist model provide itself demos and improve with RL in sim. Results in over 50% improvement with 0 human effort (6/N)

Step 4: Transfer over to the real world, either zero-shot or with some co-training. Shows scaling laws as more experience is encountered, and robust performance across distractors, object positions, visual conditions and disturbances. (5/N)

Step 3: Providing even 10 demos per env is still expensive. By training generalists from RL data, we get cross-environment generalization that allows the model to provide *itself* demos and only use human effort when necessary. The better the generalist gets, the less human effort is required. (4/N)

Step 2: Train policies on these environments with demo-bootstrapped RL. A couple of demos are needed to guide exploration, but the heavy lifting is done with large scale RL in simulation . This takes success rates from 2-3% to >90% success from <10 human demos. (3/N)

Step 1: Collect lots of environments with video scans - anyone can do it with their phone. I even had my parents scan in a bunch :) use 3D reconstruction methods like Gaussian splats to make diverse, visually & geometrically realistic sim environments for training policies (2/N)

I'm excited about scaling up robot learning! We’ve been scaling up data gen with RL in realistic sims generated from crowdsourced videos. Enables data collection far more cheaply than real world teleop. Importantly, data becomes *cheaper* with more environments and transfers to real robots! 🧵 (1/N)