README

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 Table of Contents

- NVIDIA Isaac GR00T
- What's New in GR00T N1.7
- Installation
- LeRobot Integration
- Model Checkpoints & Embodiment Tags
- Data Format
- Inference
- Fine-tuning
- Evaluation
- Contributions
- License
- Citation

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 NVIDIA Isaac GR00T


  
    
      
    
    
      
    
    
      
    
  


 We just released GR00T N1.7 General Availability, the latest version of GR00T N1 with a new VLM backbone (Cosmos-Reason2-2B / Qwen3-VL) and improved performance.

 This is a General Availability (GA) release. You are welcome to download the model, explore the codebase, and build on the stack, with full support and stability guarantees.

 What's available:
 - Pre-trained GR00T N1.7 model weights and reference code
 - Fine-tuning and inference with custom robot data or demonstrations
 - Experimentation, prototyping, and research use cases
 - Production deployment with commercial support
 - Complete benchmarks and a fully validated, stable feature set
 - Pull request contributions

 We welcome feedback - please feel free to raise issues and pull requests in this repository.

 Previous releases: N1.6 | N1.5

NVIDIA Isaac GR00T N1.7 is an open vision-language-action (VLA) model for generalized humanoid robot skills. This cross-embodiment model takes multimodal input, including language and images, to perform manipulation tasks in diverse environments.

GR00T N1.7 is trained on a diverse mixture of robot data including bimanual, semi-humanoid and an expansive humanoid dataset. It is adaptable through post-training for specific embodiments, tasks and environments.

GR00T N1.7 is fully commercially licensable under Apache 2.0. It delivers comparable performance to N1.6, with improved generalization and language-following capabilities driven by the inclusion of 20K hours of EgoScale human video data in pretraining.

GR00T N1.7 runs at ~40 Hz on NVIDIA RTX PRO 6000 Blackwell Server Edition and ~28 Hz on Jetson AGX Thor with TensorRT. Benchmark details (batch-1 LIBERO; Thor MAXN + jetson_clocks).

The neural network architecture of GR00T N1.7 is a combination of vision-language foundation model and diffusion transformer head that denoises continuous actions. Here is a schematic diagram of the architecture:





 Workflow Overview

1. Prepare data — Collect robot demonstrations (video, state, action) and convert them to the GR00T LeRobot format. Demo datasets are included for quick testing.
2. Run inference — Try zero-shot inference with the base model on pretrain embodiments, or use a finetuned checkpoint for benchmark tasks.
3. Fine-tune — Adapt the model to your robot using launch_finetune.py with your own data and modality config.
4. Evaluate — Validate with open-loop evaluation, then test in simulation benchmarks or on real hardware via the Policy API.
5. Deploy — Connect Gr00tPolicy to your robot controller, optionally accelerated with TensorRT