Researcher's Guide to Physical AI

Researcher's Guide to Physical AI

Every week, a deep-yet-accessible 15-minute briefing on what happened in Physical AI: robot foundation models, humanoids, manipulation, simulation, world models, and the research behind them. Built for researchers and curious newcomers alike, every episode gives you the background you need, then goes deep with concrete examples. Each episode ships with a long-form written deep dive. Every episode ships with a long-form written deep dive, full method details, benchmark numbers and links to every paper, at https://physicalaipodcast.co

Episodes

September 5, 2026 16 mins
NVIDIA agreed to buy Hugging Face for just under thirteen billion dollars, which puts the shelf almost every open robot model sits on inside the company that sells the chips those models train on. Its own announcement of the deal never uses the word robotics, not once, and three days earlier a small company in Oslo opened a graded, licensed marketplace for humanoid training data in Hugging Face's robot file format. Also: Skild clai...
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Anthropic previewed a driver specification that would let any model discover and operate a microscope, a laser or a robot arm, and in the limitations section of the same announcement it says the model can't reliably predict that shaking a liquid makes bubbles. On the same day, a federal judge threw out the Pentagon's blacklisting of the company for refusing to let its models drive autonomous weapons. Also: Unitree gave back half it...
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Anthropic has told its bankers it expects the largest first-time share sale in history, and it is reportedly still trying to buy a world-model company for six billion dollars while it does. A private lab can fund a five-year bet on physics, and a public one answers to a quarterly clock, which makes this the most consequential open question in Physical AI right now. Also: Unitree closed its first day of trading up 460 percent on a s...
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About twenty researchers spent a paper writing down, formally, the rule that when you tell a learned simulator to move the robot's arm, the arm should move. Six of the open-source world models the field builds on don't reliably obey, and a second paper explains why: trained only on successful demonstrations, they've never had a reason to learn what a bad action does, so feed them one and they show you the task succeeding anyway. Al...
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Two teams tested, independently and in the same week, whether the video-generating world models robotics is betting on actually understand physics. They get the shape of a falling mug right and the numbers underneath it wrong, which matters, because these are the simulators meant to replace real robot data. Also: why choosing your training video beats collecting more of it, a robot that spots a fake instruction and obeys it anyway,...
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