NVIDIA wants to be the "Android" of "Physical AI"

NVIDIA is fully committed to creating the default platform in the robotics field, aiming to replicate Android's dominance in smartphone operating systems.

On January 5th, NVIDIA unveiled multiple open-source foundational models at CES 2026, including several open-source foundational models that enable robots to reason, plan, and adapt across various tasks and environments, all of which are available on the Hugging Face platform.

NVIDIA also launched the next-generation Jetson T4000 graphics card based on the Blackwell architecture, along with an open-source command center named OSMO to support the entire robotics development workflow. The company has deepened its collaboration with Hugging Face, aiming to lower the hardware threshold and technical barriers for robot training.

This layout reflects the industry trend of artificial intelligence migrating from the cloud to the physical world. As sensor costs decrease, simulation technology advances, and AI model generalization capabilities improve, robots are evolving from executing single tasks to becoming more generalized. Companies like Boston Dynamics and Caterpillar have begun using NVIDIA technology, and the robotics category has become the fastest-growing area on the Hugging Face platform.

Building a Complete Model Matrix

The foundational models released by NVIDIA constitute the core capability layer of physical AI.

The world models Cosmos Transfer 2.5 and Cosmos Predict 2.5 are responsible for synthetic data generation and robot strategy evaluation, allowing for the validation of robot behavior in simulated environments.

Cosmos Reason 2, as a reasoning-based visual language model, empowers AI systems with the ability to observe, understand, and act in the physical world.

Isaac GR00T N1.6 is a visual language action model specifically developed for humanoid robots, using Cosmos Reason as the reasoning core to achieve full-body control, enabling humanoid robots to perform movement and object manipulation simultaneously.

The Isaac Lab-Arena launched by NVIDIA at CES is an open-source simulation framework hosted on GitHub, aimed at addressing the industry's pain points in robot capability validation.

As robots learn complex tasks such as precise object handling and cable installation, validating these capabilities in physical environments is often costly, time-consuming, and risky.

This platform integrates resources, task scenarios, training tools, and existing benchmarks like Libero, RoboCasa, and RoboTwin, establishing a universal framework for an industry that previously lacked unified standards. The accompanying open-source platform OSMO serves as a command center, integrating the entire workflow from data generation to training, supporting both desktop and cloud environments.

Lowering Hardware Thresholds

The new member of the Thor series, the Jetson T4000 graphics card, is equipped with the Blackwell architecture and serves as a cost-effective device-side computing power upgrade solution, providing 12 trillion floating-point AI operations and 64GB of memory, with power consumption controlled between 40 to 70 watts NVIDIA has also deepened its collaboration with Hugging Face by integrating Isaac and GR00T technologies into the latter's LeRobot framework, connecting NVIDIA's 2 million robot developers with Hugging Face's 13 million AI builders.

The open-source humanoid robot Reachy 2 now directly supports NVIDIA's Jetson Thor chip, allowing developers to test different AI models without being locked into proprietary systems.

Early signs indicate that NVIDIA's strategy is yielding results. Robotics has become the fastest-growing category on the Hugging Face platform, with NVIDIA's models leading in download numbers. Companies such as Boston Dynamics, Caterpillar, Franka Robots, and NEURA Robotics are already utilizing NVIDIA technology.

This layout reflects the company's strategic intent to make robot development more accessible while positioning itself as a supplier of underlying hardware and software, similar to the role of Android for smartphone manufacturers.

As AI shifts from the cloud to machines capable of learning in the physical world, cheaper sensors, advanced simulation technologies, and AI models that generalize across tasks are driving a transformation across the industry