By Mukundan Sivaraj · AIM Media House
A robot runs an espresso machine for thirteen hours straight in a lab. It folds mixed laundry at a rate of ~3 minutes per item, and assembles boxes at ~2.5 minutes per unit.
These runs were posted by Physical Intelligence (PI) as evidence of its new model, π*0.6, which the company says “more than doubles throughput over a base model trained without RL”.
If accurate, this is among the clearest claims yet of robots that learn and improve via real-world experience rather than just being pre-programmed.
The effort matches PI’s longstanding aim: “general purpose robots doing general purpose tasks in the real world.” according to co-founder Brian Ichter at Robotics Fellows Forum 2025.
This update builds on two years of work at the company and sits at the center of its effort to develop general-purpose robotic intelligence. How PI Collected Its First Real-World Data Physical Intelligence was founded in early 2024 by researchers including Brian Ichter, Karol Hausman, Chelsea Finn and Sergey Levine.
The company describes its mission as building foundation models and learning algorithms “to bring general-purpose AI into the physical world.” In November 2024 PI announced a US$400 million funding round at a US$2 billion valuation, with backers including Jeff Bezos and OpenAI.
Early work focused on collecting large volumes of data and constructing a generalist robot policy architecture.
As Ichter described: “We started with a large open-source dataset and then we collected a lot of data through teleoperation.” Through that work the company built the initial model, π₀, then π₀.5, which could perform tasks in new homes.
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