Physical Intelligence

To find out more, check out our blog post, videos, and full-length research paper: pi.website/pi07

We are still discovering what π0.7 can do. It's fun to play with and the results so far have been quite surprising!

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!

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.

To learn more about RLT, check out our blog post: pi.website/research/rlt

With 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.

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.

I'm extremely excited to announce that we've successfully inferenced π0.5 on our excavator! We've collected a massive corpus of real-world data with natural language labels from operators in the industry and are using it to create some really cool policies. Here's our first demo of it successfully completing a task with just 200 trajectories. More on the way :) Read our blog post: labs.actor/research/vla-e… @physical_int

For more, check out our paper: pi.website/download/Mem.p… our blog post: pi.website/research/memory

Apart from solving new tasks, memory also allows our policies to be more robust: we show early signs of in-context adaptation, where the robot learns to adapt its behavior on-the-fly by learning from its past mistakes.

We’ve developed a memory system for our models that provides both short-term visual memory and long-term semantic memory. Our approach allows us to train robots to perform long and complex tasks, like cleaning up a kitchen or preparing a grilled cheese sandwich from scratch 👇