Rohan Paul (@rohanpaul_ai) · 10/13/2026, 15:08:59
Nature Will Releases Natura-1: A 50B MoE Model Using 1M Hours of Egocentric Human Data for Zero-Shot Dexterous Robot Control
Nature Will introduces Natura-1, a 50-billion-parameter Mixture-of-Experts foundation model for the physical world. Trained on 1 million hours of egocentric human video, it addresses key robotics challenges including fine two-handed control, zero-shot task generalization, and long-horizon memory recovery, enabling direct mapping from natural language instructions to dexterous robot hand actions.
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Rohan Paul (@rohanpaul_ai) · 2026-10-13 07
The hardest thing to give a robot is a capable pair of hands, and Natura-1, from @naturewill is a strong attempt at it.
Nature Will trained their new Natura-1 on 1M hours of egocentric human data
first-person video of people using their hands, then used that to control real robot hands
Robot demonstrations are slow to collect, so human footage is the faster route to scale, provided the skills survive the move from human hands to robot hands.
One model handles 3 things robots usually fail at together: fine 2-handed control after the grasp, new tasks without extra training, and long sequences where it has to remember earlier steps and recover from mistakes. Many robot systems show 1 of these in a narrow setting.
↳ 引用 @naturewill (nature will): Introducing Natura-1, our first general foundation model for the physical world.
It follows natural-language instructions zero-shot and turns them into action with dexterous hands.
Natura-1 is a 50-billion-parameter mixture-of-experts (MoE) model pretrained on one million hours of egocentric human experience.
On top of this foundation, we conduct efficient post-training on a limited amount of robot data to adapt pretrained world knowledge to dexterous manipulation, yielding a model that generalizes to a wide range of unseen scenarios.
Natura-1 follows complex natural-language instructions zero-shot, reasoning through spatial constraints, multi-object relationships, and negative conditions.
It can also autonomously carry out long and continuous workflows to perform long-horizon executions.
Natura-1 provides early evidence that generalization—and even emergent behavior—can coexist with the complexity of dexterous hands.
Further progress depends on the full stack: infrastructure for faster training and real-time inference, pretraining data at greater scale and coverage, and models that transfer to robots with less adaptation.
We expect it to be a long-term research and engineering effort.
