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Nature Will launches 50-billion-parameter AI model for dexterous robot hands

Close-up of hands assembling a robotic project with cables indoors, showcasing technology and electronics.
Illustrative photo.Photo by Vanessa Loring on Pexels

What happened

Nature Will, a physical AI lab dedicated to building physical AGI, has announced the launch of Natura-1, a 50-billion-parameter physical general foundation model for dexterous hand control and zero-shot instruction following in advanced robotics applications. November 9, 2026 by Sam Francis Nature Will , a physical AI lab dedicated to building physical AGI, has announced the launch of Natura-1, a 50-billion-parameter physical general foundation model for dexterous hand control and zero-shot instruction following in advanced robotics applications.

Pre-trained on over 1 million hours of egocentric human data, the Natura-1 combines instruction following with the ability to manipulate to changing task conditions and sustained actions over extended interactions, bringing the versatility of the hand within reach of a model that connects human intent to physical action. With the rapid advancements in general AI and large language models, AI models developed for physical applications represent the next frontier in technological progress.

Natura-1 showcases that generalization and fine physical control can be learned together in a foundation model for dexterous hands, applicable to any field where fine or delicate manipulations are required as part of the creation process. Built on a 50 billion parameter mixture-of-experts model trained jointly on language, images, video, and action, Natura-1 operates at a 100-millisecond control loop to unify language, vision, and high-degree-of-freedom action that connects language-directed behavior with the physical control.

Key facts

  • Nature Will, a physical AI lab dedicated to building physical AGI, has — announced: the launch of Natura-1, a 50-billion-parameter physical general foundation model for dexterous hand control and zero-shot instruction following in advanced robotics applications

Sources & evidence