But first: The core claim in the paper is compositional generalization — the ability to combine skills learned in different contexts to solve problems the model has never encountered. Until now, the standard approach to robot training has been essentially rote memorization — collect data on a specific task, train a specialist model on that data, then repeat for every new task. π0.7, Physical Intelligence says, breaks that pattern.

“Once it crosses that threshold where it goes from only doing exactly the stuff that you collect the data for to actually remixing things in new ways,” says Sergey Levine, a co-founder of Physical Intelligence and a UC Berkeley professor focused on AI for robotics, “the capabilities are going up more than linearly with the amount of data. That much more favorable scaling property is something we’ve seen in other domains, like language and vision.”

The paper’s most striking demonstration involves an air fryer the model had essentially never seen in training. When the research team investigated, they found only two relevant episodes in the entire training dataset: one where a different robot merely pushed the air fryer closed, and one from an open source dataset where yet another robot placed a plastic bottle inside one on someone’s instructions. The model had somehow synthesized those fragments, plus broader web-based pretraining data, into a functional understanding of how the appliance works.

“It’s very hard to track down where the knowledge is coming from, or where it will succeed or fail,” says Lucy Shi, a Physical Intelligence researcher and Stanford computer science Ph.D. student. Still, with zero coaching, the model made a passable attempt at using the appliance to cook a sweet potato. With step-by-step verbal instructions — essentially, a human walking the robot through the task the way you might explain something to a new employee — it performed successfully.