NVIDIA's GTC 2025 Announcement for Physical AI Developers: New Open Models and Datasets

At its annual GTC conference, NVIDIA has unveiled a trio of groundbreaking open-source releases aimed at accelerating physical AI development. Release of a new suite of world foundation models (WFMs) with multicontrols called Cosmos Transfer , a highly curated Physical AI Dataset , and the first open model for general humanoid reasoning called NVIDIA Isaac GR00T N1 - represent a significant leap forward in physical AI technology, offering developers powerful tools and resources to advance robotics systems, and enhance autonomous vehicle technology.

Cosmos Transfer , the latest addition to NVIDIA's Cosmos™ world foundation models (WFMs), introduces a new level of control and accuracy in generating virtual world scenes.

Available in 7 billion parameter size, the model utilizes multicontrols to guide the generation of high-fidelity world scenes from structural inputs, ensuring precise spatial alignment and scene composition.

Isaac GR00T N1 uses a single model and set of weights to enable manipulation behaviors on various humanoid robots, such as the Fourier GR-1 and 1X Neo . It demonstrates robust generalization across a range of tasks, including grasping and manipulating objects with one or both arms, as well as transferring items between arms. It can also execute complex, multi-step tasks that require sustained contextual understanding and the integration of diverse skills. These capabilities make it well-suited for applications in material handling, packaging, and inspection.

Isaac GR00T N1 features a dual-system architecture inspired by human cognition, consisting of the following complementary components:

The NVIDIA Isaac GR00T-N1-2B model is available on Hugging Face . Sample datasets and PyTorch scripts for post-training using custom user datasets, which is compatible with the Hugging Face LeRobot format are available on GitHub . For more information about the Isaac GR00T N1 model, see the research paper .

The model is built by training individual ControlNets separately for each sensor modality used to capture the simulated world.

Input types include 3D bounding box map, Trajectory map, Depth map, Segmentation map.

Cosmos Transfer coupled with the NVIDIA Omniverse platform is driving controllable synthetic data generation for robotics and autonomous vehicle development at scale. Find more Cosmos Transfer Examples on GitHub.