How Physical AI Learns Across Language, Video and Action — Ming-Yu Liu

The car making a left turn at the start of this episode was never filmed. Cosmos 3 generated it. Ming-Yu Liu, who leads the Cosmos research at NVIDIA, explains how one model can describe a video, generate one, and produce robot actions.

He walks Tim through the architecture. A vision language model reasons one token at a time; its weights then initialise a bidirectional diffusion generator for video, audio and action, and a shared temporal position scheme lines up signals that run at different rates. Ming-Yu treats "world model" as a set of tools, not one definition: forward dynamics, inverse dynamics and policy, trained together under a capacity limit so that each helps the others. He also explains why plentiful first-person human video carries over to robots, which have far less data of their own, and why a Cosmos model post-trained on the DROID dataset is a good starting point for pick-and-place policies.

The most practical thread is testing. A neural simulator does not need accurate success rates. It only needs to rank policy A above policy B the way the real world would, so a team can narrow down which checkpoints deserve a real trial. Cosmos Dreams applies that closed-loop idea to driving and robotics, and Ming-Yu argues that humanoids around children and pets make safety matter even more than it does for cars. The conversation ends on the Super, Nano and Edge sizes (Edge targets Jetson Thor, Orin and DGX Spark) and where to find the open weights, code and data.

This episode is a paid partnership with NVIDIA.

Learn more about Cosmos: https://nvda.ws/4cJoY1S

Explore Cosmos Lab: https://research.nvidia.com/labs/cosmos-lab/cosmos3/

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TIMESTAMPS:

00:00:00 A road that was never filmed

00:02:28 Inside Cosmos 3: reasoning and generator towers

00:05:02 World models: dynamics, policy and one clock

00:08:59 Learning robot skills from human video

00:11:06 Ambiguous tasks and system 2 planning

00:12:53 Neural simulators for policy verification

00:16:41 Cosmos as a starting point for robot policies

00:19:00 Cosmos Dreams and robot safety

00:22:04 Super, Nano and Edge model sizes

00:24:24 Open models, the Cosmos repo and feedback

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REFERENCES:

tool:

[00:00:13] Cosmos 3 (NVIDIA Cosmos Lab project page)

https://research.nvidia.com/labs/cosmos-lab/cosmos3/

[00:18:27] NVIDIA Cosmos GitHub repository

https://github.com/NVIDIA/cosmos

[00:22:05] Cosmos3-Edge model card

https://huggingface.co/nvidia/Cosmos3-Edge

[00:22:15] Cosmos3-Super model card

https://huggingface.co/nvidia/Cosmos3-Super

[00:22:16] Cosmos3-Nano model card

https://huggingface.co/nvidia/Cosmos3-Nano

[00:22:50] NVIDIA Jetson Thor

https://www.nvidia.com/en-us/autonomous-machines/embedded-systems/jetson-thor/

[00:22:52] NVIDIA Jetson Orin

https://www.nvidia.com/en-us/autonomous-machines/embedded-systems/jetson-orin/

[00:22:53] NVIDIA DGX Spark

https://www.nvidia.com/en-us/products/workstations/dgx-spark/

[00:24:42] Cosmos 3 collection on Hugging Face

https://huggingface.co/collections/nvidia/cosmos3

other:

[00:01:07] Cosmos-Dreams closed-loop simulators (NVIDIA SIGGRAPH 2026 blog)

https://blogs.nvidia.com/blog/siggraph-news-2026/

paper:

[00:08:54] Cosmos 3: Omnimodal World Models for Physical AI

https://arxiv.org/abs/2606.02800

[00:17:43] DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset

https://arxiv.org/abs/2403.12945

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RESCRIPT: https://app.rescript.info/share/e2385948cf465f0d6a2c0930150fc3ab