Nvidia Says 'the Age of Generalist Robotics Is Here'

During the company's GTC 2025 keynote today, Nvidia founder and CEO Jensen Huang announced Isaac GR00T N1 -- the company's first open-source, pre-trained yet customizable foundation model designed to accelerate the development and capabilities of humanoid robots. 'The age of generalist robotics is here,' said Huang. 'With Nvidia Isaac GR00T N1 and new data-generation and robot-learning frameworks, robotics developers everywhere will open the next frontier in the age of AI.' The Verge reports: Huang demonstrated 1X's NEO Gamma humanoid robot performing autonomous tidying jobs using a post-trained policy built on the GR00T N1 model. Other companies developing humanoid robots who have had early access to the GR00T N1 model include Boston Dynamics, the creators of Atlas; Agility Robotics; Mentee Robotics; and Neura Robotics. Originally announced as Project GR00T a year ago, the GR00T N1 foundation model utilizes a dual-system architecture inspired by human cognition.

System 1, as Nvidia calls it, is described as a 'fast-thinking action model' that behaves similarly to human reflexes and intuition. It was trained on data collected through human demonstrations and synthetic data generated by Nvidia's Omniverse platform. System 2, which is powered by a vision language model, is a 'slow-thinking model' that 'reasons about its environment and the instructions it has received to plan actions.' Those plans are passed along to System 1, which translates them into 'precise, continuous robot movements' that include grasping, moving objects with one or two arms, as well as more complex multistep tasks that involve combinations of basic skills.

While the GR00T N1 foundation model is pretrained with generalized humanoid reasoning and skills, developers can customize its behavior and capabilities for specific needs by post-training it with data gathered from human demonstrations or simulations. Nvidia has made GR00T N1 training data and task evaluation scenarios available for download through Hugging Face and GitHub.

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