Physical Intelligence
In plain wordsA San Francisco company, founded in 2024, that builds general-purpose AI 'brains' meant to control many different kinds of robots doing many different tasks.
An American robotics AI company that develops robot foundation modelsrobot foundation model, n.: One large AI model trained on huge amounts of robot and internet data so that it can control many robots on many tasks, and be adapted quickly to new ones.: single generalist policiesgeneralist robot policy, n.: A single robot-control program trained to do many different tasks in many places, often on several kinds of robots: the opposite of a specialist trained for one job. intended to control many kinds of robot bodies across many tasks. Its π0π0, prop. n.: A general-purpose AI model for controlling robots, released in 2024 by the start-up Physical Intelligence, that sees through cameras, follows typed or spoken instructions and moves robot arms deftly enough to fold laundry. model (October 2024), a vision-language-action modelvision-language-action model, n.: An AI model that looks through a robot's cameras, reads an instruction such as 'put the cup in the sink', and turns both straight into the robot's movements. that pairs a pretrained vision-language model with an 'action expert' producing continuous motor commands by flow matchingflow matching, n.: A way to train generators that learns, for every point between random noise and real data, which direction to move, so that noise can be steered smoothly into new examples., was trained on data from many robot typescross-embodiment learning, n.: Training one robot AI on data from many differently shaped robots, such as arms, robot dogs, drones and humanoids, so that skills learned on one body help another. and demonstrated tasks such as clearing tables, folding laundry and assembling boxes; its weights were released in February 2025. Successors include π0.5 (2025), which generalized to cleaning unfamiliar homes, and π*0.6 (2025), refined by reinforcement learningreinforcement learning, n.: A way of teaching a computer program by trial and error: it tries actions, earns points for good results and penalties for bad ones, and gradually learns which choices pay off over time. from the robot's own experience.
Origin
Founded in 2024 in San Francisco by a group of robot-learning researchers and entrepreneurs including Karol Hausman, its chief executive, Sergey Levine, Chelsea Finn, Brian Ichter and Lachy Groom; several had worked on Google's robotics research.
Related
- π0π0, prop. n.: A general-purpose AI model for controlling robots, released in 2024 by the start-up Physical Intelligence, that sees through cameras, follows typed or spoken instructions and moves robot arms deftly enough to fold laundry.
- vision-language-action modelvision-language-action model, n.: An AI model that looks through a robot's cameras, reads an instruction such as 'put the cup in the sink', and turns both straight into the robot's movements.
- robot foundation modelrobot foundation model, n.: One large AI model trained on huge amounts of robot and internet data so that it can control many robots on many tasks, and be adapted quickly to new ones.
- generalist robot policygeneralist robot policy, n.: A single robot-control program trained to do many different tasks in many places, often on several kinds of robots: the opposite of a specialist trained for one job.
- robot learningrobot learning, n.: Teaching robots skills from data and experience, such as watching demonstrations, practicing by trial and error or training in simulation, instead of programming every movement by hand.
- cross-embodiment learningcross-embodiment learning, n.: Training one robot AI on data from many differently shaped robots, such as arms, robot dogs, drones and humanoids, so that skills learned on one body help another.
Mentioned in
Chelsea FinnChelsea Finn, prop. n.: An American computer scientist at Stanford known for 'learning to learn' methods that let AI adapt to new tasks from a few examples, and a co-founder of the robotics company Physical Intelligence., Sergey LevineSergey Levine, prop. n.: A Berkeley computer scientist, one of the most prolific researchers on how robots can learn skills from data and experience, and a co-founder of the robotics company Physical Intelligence.