NVIDIA Isaac GR00T N1 is the open foundation model for humanoid robots. Multimodal input (language, images), generates actions. Includes SIM frameworks and data pipelines.
Check out something truly groundbreaking: Isaac GR00T N1 from NVIDIA – they're calling it the world's first open foundation model for general-purpose humanoid robot reasoning and skills! The goal here is to democratize Physical AI.
What's so special about this? It's a single neural network that goes from "photons to actions" – taking in images and language, and outputting continuous control signals for a robot. And it's designed to be general, not just for one specific task or robot.
They've trained it on a massive and diverse dataset:
Real humanoid teleoperation data.
Synthetic data generated in simulation (they're open-sourcing 300K+ trajectories!).
"Neural trajectories" – using video generation models to create even more training data with accurate physics.
Latent actions extracted from in-the-wild human videos.
They've even developed new algorithms to extract "action tokens" from videos.
The architecture is also interesting: it's a "System 1, System 2" setup. System 2 (a Vision-Language Model) understands the scene and the instructions, while System 1 (a Diffusion Transformer) handles the fast, precise motor control.
NVIDIA is now empowering the next generation of humanoid robots with these open foundations, don't underestimate the impact of this.
@masump Think of it like this: System 2 is the "brain" (planning), and System 1 is the "body" (fast, precise action). They're trained together on lots of data to work seamlessly.
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Flowtica Scribe
Hi everyone!
Check out something truly groundbreaking: Isaac GR00T N1 from NVIDIA – they're calling it the world's first open foundation model for general-purpose humanoid robot reasoning and skills! The goal here is to democratize Physical AI.
What's so special about this? It's a single neural network that goes from "photons to actions" – taking in images and language, and outputting continuous control signals for a robot. And it's designed to be general, not just for one specific task or robot.
They've trained it on a massive and diverse dataset:
Real humanoid teleoperation data.
Synthetic data generated in simulation (they're open-sourcing 300K+ trajectories!).
"Neural trajectories" – using video generation models to create even more training data with accurate physics.
Latent actions extracted from in-the-wild human videos.
They've even developed new algorithms to extract "action tokens" from videos.
The architecture is also interesting: it's a "System 1, System 2" setup. System 2 (a Vision-Language Model) understands the scene and the instructions, while System 1 (a Diffusion Transformer) handles the fast, precise motor control.
NVIDIA is now empowering the next generation of humanoid robots with these open foundations, don't underestimate the impact of this.
Flowtica Scribe
@masump Think of it like this: System 2 is the "brain" (planning), and System 1 is the "body" (fast, precise action). They're trained together on lots of data to work seamlessly.