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NVIDIA Isaac GR00T N1

The first open humanoid-robot foundation model: a dual-system VLA (fast System 1 + reasoning System 2), announced at GTC 2025.

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About

Isaac GR00T N1, announced by NVIDIA at GTC on March 18, 2025, was pitched as the world's first open, fully customizable foundation model for generalized humanoid reasoning and skills. It's a vision-language-action model of roughly 2 billion parameters with a dual-system architecture inspired by human cognition: a slow-thinking System 2 (a vision-language model that reasons about the environment and plans actions) paired with a fast-thinking System 1 (a flow-matching diffusion transformer that turns plans into precise, continuous motor commands). System 1 was trained on human demonstration data plus massive synthetic data from NVIDIA Omniverse.

NVIDIA released the weights, training data, and benchmark tasks on Hugging Face and GitHub, and has since iterated fast: N1.5 (June 2025), N1.6 (September 2025), and N1.7 (April 2026), which moved to a commercial-friendly NVIDIA Open Model License. The original N1 shipped under non-commercial terms, so check the license of the exact checkpoint you use. GR00T N1 generalizes across grasping, single- and dual-arm manipulation, and multi-step material-handling tasks — provider-claimed capabilities, since NVIDIA published no standardized benchmark scores for it.

Specs

Context window

N/A — vision-language-action model (images + language in, continuous actions out)

Pricing

Free / open weights (custom NVIDIA license; commercial use added with GR00T N1.7)

Free quota

Weights downloadable from Hugging Face after license acceptance

API access

Hugging Face (NVIDIA org, gated license acceptance); Isaac Lab / Isaac Sim; self-hosted

Weights

Open weights

Released

Not disclosed

Benchmarks

  • No standardized public benchmark scores published for GR00T N1
  • NVIDIA-reported: generalization across grasping, moving objects with one or both arms, and multi-step manipulation tasks (provider claim)

Provider-reported unless a source says otherwise. Benchmarks are marketing-adjacent by nature — treat them as directional.

Use cases

roboticshumanoid-robotsembodied-aimanipulationresearch

Trust score — 8/10

/10 trustVerified

Scored 8/10 (verified tier, metrics-v1). Evidence: NVIDIA GTC announcement Mar 18, 2025 (press release); dual-system VLA architecture documented in the GR00T N1 white paper (arXiv 2503.14734); weights + training data + benchmarks released on HF/GitHub; iterated through N1.5/N1.6/N1.7 with 20K+ hours of training data by 2026. Open weights. Release facts corroborated across NVIDIA, press, and community fine-tune reports. Strengths: NVIDIA's full platform weight behind it, rapid iteration, real robotics-developer ecosystem. Docked for: (1) original N1 weights are non-commercial — the permissive license only arrived with N1.7; (2) no published standardized benchmarks, so capability claims are NVIDIA's own. License: custom NVIDIA license (non-commercial at N1 launch; commercial use permitted from N1.7).

How the trust score is built

Full methodology →
Adoption · stars/downloads, log-scaled vs peers
9.0/10
Maintenance · recency, releases, issue load
9.0/10
Openness · license + docs
6.0/10
Manual review · sanity read + red-flag check
8.0/10
  • · Announced at GTC Mar 18, 2025; ~2B-param dual-system VLA (System 2 VLM planner + System 1 diffusion action model)
  • · White paper arXiv 2503.14734; weights + training data + benchmarks released on HF/GitHub
  • · Iterated N1.5 (Jun 2025) -> N1.6 (Sep 2025) -> N1.7 (Apr 2026, commercial license)
  • · No standardized published benchmarks; capability claims are NVIDIA's own

Sources