Physical Intelligence (Pi), bringing AI into the physical world.
San Francisco, CA
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Announcing the beta release of the Pi API. We’ve partnered with companies across diverse industries to adapt our models to their robots and applications, from harvesting strawberries to assembling servers. Now we’re opening access to more teams.
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Upload robot data, fine-tune our most capable models and connect your robots to cloud hosted inference through the API. We handle the infrastructure so you can focus on what’s unique to your use case.
See what teams are building and join the beta at pi.website/blog/api-beta
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Excited to see our partners at @Ultraroboticsco announce their $50M Series A! Ultra is a great example of what's becoming possible when general-purpose robot foundation models meet real-world deployments!
Robotics has a distribution problem. Today, Ultra is announcing $62M in funding to solve it, including a $50M Series A led by @hiFramework.
We believe that deployment is the bottleneck in robotics.
It’s relatively easy to make a robot look impressive in a controlled environment. It’s much harder to put a robot into the open world, make it useful, keep it running, earn the trust of the people working alongside it, and do that over and over and over again.
So that’s what we’re focusing on.
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Physical Intelligence retweeted
4. Physical Intelligence X Deft: factory-proven foundation models
Deft Robotics and Physical Intelligence are bringing general-purpose robot intelligence into real automotive factories.
The task is line feeding: repeatedly picking up parts and placing them onto the production line in the correct position and orientation.
It sounds simple. But parts shift around in the material bin as they’re picked. Each pick presents a different position and orientation, making fixed, rules-based automation impractical to deploy at-scale.
Physical Intelligence’s robot foundation models, paired with Deft’s hardware and deployment stack, let the robot adapt to this variation.
The robot locates each part, adjusts its grasp, and rotates it into position for placement—feeding the line without requiring every part to start in a predefined location.
Across both deployments: 500 hours of operation and more than 10,000 parts processed.
Every intervention surfaces the failures that matter, so the next model learns from real production failures.
It's this iterative loop of deploying, intervening, retraining, and redeploying that makes the robot better over time — not just the cleanest demo.
Robot intelligence becomes useful when it survives the factory.
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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📷
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The 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
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Joint work with the NVIDIA Cosmos team.
Paper: arxiv.org/abs/2606.18610
Website: weichengtseng.github.io/sc3-…
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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!
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We are still discovering what π0.7 can do. It's fun to play with and the results so far have been quite surprising!
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To find out more, check out our blog post, videos, and full-length research paper: pi.website/pi07
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