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Physical AI Lab Nature Will Unveils Natura-1, First General Foundation Model for the Physical World
Prioritizing dexterous workflow and zero-shot instruction following, the Natura-1 is Nature Will's first Physical General Foundation Model aimed at pioneering advanced robotics applications.
SAN FRANCISCO, CA / ACCESS Newswire / October 16, 2026 / Nature Will, a physical AI lab dedicated to building physical AGI, today 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.
"We believe general models that can handle every task must exist, and that those models must utilize dexterous hands," said Ning Ding, CEO of Nature Will. "Natura-1 is our first step towards models that scale from human experience, building infrastructure that enables us to create the model from the ground up and that allows it to scale to any application. The Natura-1 is a 50-billion-parameter mixture-of-experts model with dexterous manipulation, long-horizon execution, and zero-shot instruction following, making it truly capable of scaling human experience into purposeful action."
The model represents Nature Will's first physical foundation model that is capable of general-purpose dexterous manipulation using real hardware, acting as a breakthrough in the development of general foundation models made to interact in the real world. Designed to scale, the model autonomously executes sustained, continuous workflows and follows complex natural language instructions, enabling it to reason through spatial constraints, multi-object relationships, and negative conditions.
For more information about the Natura-1, please read the official blog at https://www.naturewill.ai
About Nature Will
Nature Will is a research-driven organization dedicated to building the physical artificial general intelligence. Focusing on training general-purpose foundation models, its team is working to develop models capable of closing the loop across perception, prediction, reasoning, and action in the real world, while exploring how embodied agents can achieve self-awareness and continuous learning within physically native environments. Nature Will's R&D stack spans data acquisition and governance, foundation model pretraining, infrastructure, simulation and real-world evaluation, post-training, and reinforcement learning. Led by Founder and CEO Ning Ding, the company has assembled a full-stack physical AI team whose core members include frontier researchers, core contributors to flagship foundation models, champions of international student supercomputing competitions, and senior robotics hardware engineers.
To learn more about Nature Will, please visit https://www.naturewill.ai/
Media Contact
Organization: Nature Will
Contact Person Name: Andrew Heibeck
Website: https://www.naturewill.ai
Email: [email protected]
City: SAN FRANCISCO
State: CA
Country: United States
SOURCE: Nature Will
View the original press release on ACCESS Newswire
M.A.Colin--AMWN