Topic 24: What is Cosmos World Foundation Model Platform?

We recently discussed Physical AI and Jensen Huang's vision for achieving it through Agentic AI. At its core, Physical AI refers to systems capable of understanding and engaging with the physical world, leveraging advanced technologies like sensor-driven agents, robotics, and physical simulation platforms. While still emerging, the growing focus on agents and robotics signals meaningful progress toward this ambitious vision.

But this progress depends on the development of World Foundation Models (WFMs) – AI systems trained to simulate real-world environments and predict outcomes from text, image, or video inputs. These models are key to creating physics-aware videos, enabling AI to better understand and interact with the physical world. There is a lot to solve there!

Just two weeks ago, NVIDIA unveiled not just a model but an entire ecosystem – they called it Cosmos. This new platform, complete with three WFMs, was also open-sourced (to be more precise it’s available under the NVIDIA Open Model License.)

Even if you’re not building robots, understanding the technologies shaping Physical AI – like NVIDIA’s Cosmos – matters. Why? Because these innovations are rewriting how AI systems learn, interact, and solve real-world problems. From smarter automation to groundbreaking simulations, the ripple effects will touch every corner of AI. Let’s dive into its components and explore the transformative potential it holds for the AI landscape in general and Physical AI in particular.

In today’s episode, we will cover:

• What is Physical AI? - A quick reminder

• World Foundation Models (WFM)

• How does Cosmos WFM platform work?

• Video Curator

• Cosmos Tokenizer

• Pre-trained WFMs

  • Diffusion WFMs

  • Autoregressive WFMs

• How good are Cosmos WFMs?

• Post-trained WFMs implementation in Physical AI applications

• What about safety or Guardrail system

• Limitations

• Conclusion

• Bonus: Resources to dive deeper

What is Physical AI? - A quick reminder

Let’s begin with the basic concept to clarify what the Cosmos WFM platform works with. Physical AI refers to AI systems equipped with sensors to perceive their environment and actuators to interact with and alter it. Embodied AI agents and robots are prime examples of this domain, designed to handle tasks that are dangerous, exhausting, or repetitive for humans.

Despite rapid advancements in many areas of AI, Physical AI has lagged behind. Mastering the complexities of physical reality remains an extraordinary challenge, requiring systems that can not only process vast sensory data but also make intelligent decisions in dynamic environments.

A crucial step toward achieving Physical AI is the development of Agentic AI – autonomous systems with the cognitive and decision-making capabilities needed to power embodied AI. These systems bridge the gap between perception and action, enabling more sophisticated interactions with the physical world.

One major obstacle is the difficulty of collecting training data for Physical AI. Real-world experimentation is often risky, expensive, and time-intensive, requiring detailed sequences of observations and actions. A promising solution to this challenge lies in World Foundation Models (WFMs) →

World Foundation Models (WFMs)

World Foundation Model (WFM) is a digital replica of the physical world where Physical AI can safely learn and practice. Some of the WFMs,

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