@QuanVng
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We got a robot to clean up homes that were never seen in its training data! Our new model, π-0.5, aims to tackle open-world generalization.
We took our robot into homes that were not in the training data and asked it to clean kitchens and bedrooms. More below⤵️View quoted post on X
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.View quoted post on X
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.View quoted post on X
Physical Intelligence (@physical_int) is building a foundation model that can control any robot to do any task — what the team describes as the GPT moment for robotics. The company's cross-embodiment approach trains across many different robot platforms, and recent results show tasks being performed zero-shot that last year required hundreds of hours of data collection.
In this episode of the @LightconePod , co-founder Quan Vuong (@QuanVng) sat down with @garrytan, @snowmaker, @sdianahu, and @harjtaggar to talk about why robotics is finally ready for its scaling moment, how PI runs its models in the cloud rather than on-device, and the playbook for what Quan sees as a Cambrian explosion of vertical robotics companies.
00:00 — Robotics just got cheaper
00:41 — The GPT moment for robotics
02:24 — Why robots didn’t work before
05:30 — The breakthrough that changed everything
09:12 — The data problem
13:33 — Robots learning without data
15:05 — Robots folding laundry (for real)
22:18 — From engineering problem → ops problem
29:12 — The startup playbook
38:46 — Thousands of robotics startups are comingView quoted post on X
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!View quoted post on X
