New work with @nvidia: evaluating robot policies entirely inside a world model. The policy acts, the model imagines the consequences, and the imagined evals predict real-world results. 🧵
real vs world-model rollout side by side📷
GIF
Physical Intelligence
122 posts
Physical Intelligence
@physical_int
Physical Intelligence (Pi), bringing AI into the physical world.
- Replying to @physical_intThe model also knows when not to trust itself: its uncertainty estimates track how far generations deviate from reality. The uncertainty accurately predicts when model generation degrades
GIFJoint work with the NVIDIA Cosmos team. Paper: arxiv.org/abs/2606.18610 Website: weichengtseng.github.io/sc3-eval/ - π0.5 driving autonomous anastomosis!
- Our newest model, π0.7, has some interesting emergent capabilities: it can control a new robot to fold shirts for which we had no shirt folding data, figure out how to use an appliance with language-based coaching, and perform a wide range of dexterous tasks all in one model!
00:00Replying to @physical_intWe are still discovering what π0.7 can do. It's fun to play with and the results so far have been quite surprising!
00:00To find out more, check out our blog post, videos, and full-length research paper: pi.website/pi07 - Physical Intelligence reposted
π, But Make It Fly ✈️ We fine-tuned π0, a VLA model pretrained entirely on manipulators, to fly a drone that picks up objects, navigates through gates, and composes both skills from language commands.
00:00 - We developed an RL method for fine-tuning our models for precise tasks in just a few hours or even minutes. Instead of training the whole model, we add an “RL token” output to π-0.6, our latest model, which is used by a tiny actor and critic to learn quickly with RL.
00:00Replying to @physical_intWith RL, the robot can learn very precise tasks, like fastening a zip tie, and can actually do it more consistently and more quickly than even human teleoperation.
00:00To learn more about RLT, check out our blog post:
