The Rise of Intelligent Machines: Nvidia Accelerates Physical AI Progress

The boundaries between artificial intelligence and the physical world are dissolving. AI systems are becoming increasingly adept at perceiving, interacting, analyzing, and responding to their physical environments.

In this AI era, intelligence has moved beyond the confines of codes or computers and has taken the form of “Physical AI” which refers to AI systems that operate beyond digital environments. It allows autonomous systems like self-driving cars, drones, and robots to understand and perform complex actions in the real (physical) world.

Nvidia is making a decisive and strategic push into physical AI. This week at the GTC 2025, Huang stated that robotics has the potential to become the largest industry in the world. The Nvidia CEO expects humanoid robots to be mainstream much earlier than five years. As physical AI is the foundation for humanoid robots, NVIDIA is positioning itself as a driving force behind this transformation.

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At the GTC event this week, Nvidia unveiled an open Physical AI dataset to advance robotics and autonomous vehicle development. The company claims that the “dataset will grow over time to become the world’s largest unified and open dataset for physical AI development.”

The dataset enables the pretraining and post-training of AI models and is available on Hugging Face. It includes 15 terabytes of data, over 320,000 trajectories for robotics training, up to 1,000 Universal Scene Description (OpenUSD) assets, and SimReady collections for realistic simulations.

Central to Nvidia’s ambitions for Physica AI is its Omniverse platform, which is a digital development platform connecting spatial computing, 3D design, and physics-based workflows. Originally designed as a simulation and visualization tool, Omniverse has evolved significantly and has now become more of an operating system for Physical AI. Users can leverage Omniverse capabilities to train autonomous systems before physical deployment.

To help facilitate the development of Physical AI, Nvidia has announced a major release of new NVIDIA Cosmos world foundation models (WFMs). These models are designed to supercharge Physical AI development by providing a foundation for training machines in a realistic virtual environment.

The Cosmos Transfer WFMs are built to ingest structured video inputs, such as lidar scans to maps, to generate controllable photoreal video outputs. This means robots and automated systems can learn more quickly, with a wider range of data, and in a safer way without relying on real-world testing.

Nvidia has also unveiled two innovative blueprints, powered by its Omniverse and Cosmos platforms, marking a significant step in the evolution of physical AI. These blueprints introduce advanced synthetic data generation engines that are essential for training autonomous systems to operate effectively in the physical world. Early adopters of these tools include 1X, Agility Robotics, Figure AI, Foretellix, Skild AI, and Uber.

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Mega, an Omniverse Blueprint now available in preview, enables large-scale testing of multi-robot fleets in industrial digital twins. It is being adopted by industry leaders like Hyundai Motor Group, Mercedes-Benz, and Foxconn to optimize automation.

One of the biggest challenges of embodied AI has been the need for massive and diverse datasets. NVIDIA Cosmos addresses this need by generating high-quality synthetic data at scale. This capability allows AI models to train on millions of realistic scenarios before interacting with the real world.

Nvidia has also unveiled a multimodal reasoning model for Physical AI called Cosmos Reason. The company claims the mode is an open and fully customizable WFM. Cosmos Reason is specifically designed to enhance the reasoning capabilities of Physical AI systems. It enables the AI system to understand how things move and interact over time and space.

“Just as large language models revolutionized generative and agentic AI, Cosmos world foundation models are a breakthrough for physical AI,” said Jensen Huang, founder and CEO of Nvidia. “Cosmos introduces an open and fully customizable reasoning model for physical AI and unlocks opportunities for step-function advances in robotics and the physical industries.”

As robotics and autonomous systems become more common, the challenge of training AI models is coming to the forefront. The highly dynamic and unpredictable nature of the physical world adds to the challenge. The latest developments in Nvidia’s Omniverse and Cosmos foundation models aim to bridge this gap by providing powerful virtual environments for large-scale AI training.

Source: Shutterstock

The use cases for Physical AI are virtually limitless. They can extend to traffic control systems, warehouses, factories, and hospitals. If Huang’s prediction about the robotics industry at GTC 2025 comes true, then Nvidia would be well placed to benefit from its early investment in this emerging field.

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