nature will

Physics shapes nature • will drives actions
Building general foundation model for the physical world.

[nature will @naturewill]

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.

[Ning Ding @stingning]

Introducing Natura-1, @naturewill's first general foundation model for the physical world.

It is a single model that follows plain-language instructions zero-shot and carries them out with dexterous hands.

- 50B MoE, pretrained on 1M hours of egocentric human experience.
- Drives 20-DoF dexterous hands from plain language, zero-shot, in a 100ms loop.
- Scaling human data from ~44K to 1M hours cut action MSE on held-out robot data by 67.7%.
- Built on in-house data and compute infra, it handles fine-grained long-horizon tasks and a ~30-minute zero-shot interaction in one continuous take.

The general model exists, and it can only get stronger.

https://t.co/mwTNE6BSK5

[nature will @naturewill]

Explore the research behind Natura-1:

https://t.co/RlqdWWNE5S

[nature will @naturewill]

30 minutes. One take. No cuts.

We put Natura-1’s zero-shot generalization to the test with complex spoken instructions and unfamiliar tasks and environments.

It responds to new requests, adjusts and retries when an action fails, and keeps going autonomously.

[nature will @naturewill]

Explore Natura-1’s real-world demos and scaling results in our technical blog, alongside a closer look at its training data, model architecture, and infrastructure.
https://t.co/wRiGuZ27Sn

[nature will @naturewill]

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.

[nature will @naturewill]

With egocentric training data scaled from 44K to 1M hours, Natura-1 predicts actions more accurately on unseen human and robot data.

Prediction MSE error on real-robot data falls by 67.7%, without fine-tuning on robot data.

[nature will @naturewill]

nature will _______.