> ## Documentation Index
> Fetch the complete documentation index at: https://mintlify.com/NVIDIA/Isaac-GR00T/llms.txt
> Use this file to discover all available pages before exploring further.
# Model overview
> Overview of the GR00T model architecture and capabilities
NVIDIA Isaac GR00T N1.6 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.
## Architecture
The neural network architecture of GR00T N1.6 is a combination of vision-language foundation model and diffusion transformer head that denoises continuous actions.
### Key components
The model consists of three main components:
1. **Vision-Language Model (VLM)**: Cosmos-Reason-2B variant that encodes images in their native aspect ratio without padding
2. **Diffusion Transformer (DiT)**: 32-layer transformer that denoises continuous action chunks
3. **Action Prediction Head**: Outputs state-relative action chunks for most embodiments
## Model capabilities
GR00T N1.6 is trained on a diverse mixture of robot data and is adaptable through post-training for specific embodiments, tasks, and environments.
### Training data
The model is pre-trained on 10,000+ hours of robot data from various embodiments:
* Bimanual YAM arms
* AGIBot Genie1
* Simulated Galaxea R1 Pro on the BEHAVIOR suite
* Whole-body locomanipulation with Unitree G1
* RoboCasa Panda robot with omron mobile base
* Fourier GR1 robot
### Cross-embodiment support
GR00T N1.6 supports multiple robot embodiments out of the box. See [Embodiment tags](/concepts/embodiment-tags) for the full list of supported robots.
## What's new in N1.6
GR00T N1.6 represents a significant upgrade over GR00T N1.5, with improvements in both model architecture and data leading to better performance.
### Architectural changes
* **Base VLM**: Uses an internal NVIDIA Cosmos-Reason-2B VLM variant with flexible resolution support
* **Larger DiT**: 2x larger DiT (32 layers vs 16 layers in N1.5)
* **Simplified architecture**: Removes N1.5's post-VLM 4-layer transformer adapter and unfreezes top 4 layers of the VLM during pretraining
* **State-relative actions**: Predicts state-relative action chunks for most embodiments, rather than absolute joint angles or EEF positions
### Performance improvements
GR00T-N1.6-3B achieves real-time inference at 27.3 Hz on RTX 5090 with torch.compile optimization.
Inference timing (4 denoising steps, single view):
| Device | Mode | Data Processing | Backbone | Action Head | E2E | Frequency |
| -------- | ------------- | --------------- | -------- | ----------- | ------ | --------- |
| RTX 5090 | torch.compile | 2 ms | 18 ms | 16 ms | 37 ms | 27.3 Hz |
| H100 | torch.compile | 4 ms | 23 ms | 11 ms | 38 ms | 26.3 Hz |
| RTX 4090 | torch.compile | 2 ms | 25 ms | 17 ms | 44 ms | 22.8 Hz |
| Thor | torch.compile | 5 ms | 39 ms | 61 ms | 105 ms | 9.5 Hz |
## Use cases
GR00T N1.6 is designed for researchers and professionals in robotics to:
* Leverage a pre-trained foundation model for robot control
* Fine-tune on small, custom datasets
* Adapt the model to specific robotics tasks with minimal data
* Deploy the model for inference on various hardware platforms
The focus is on enabling customization of robot behaviors through finetuning rather than training from scratch.
## Model checkpoints
### Base models
Pre-trained base VLA model checkpoints are available for finetuning:
* **GR00T N1.6**: [nvidia/GR00T-N1.6-3B](https://huggingface.co/nvidia/GR00T-N1.6-3B) (3B parameters)
* **GR00T N1.5**: [nvidia/GR00T-N1.5-3B](https://huggingface.co/nvidia/GR00T-N1.5-3B) (3B parameters)
### Finetuned models
Finetuned checkpoints are available for various robot platforms and benchmarks:
* **GR00T-N1.6-bridge**: Fine-tuned on Bridge dataset for WidowX robot
* **GR00T-N1.6-fractal**: Fine-tuned on Fractal dataset for Google robot
* **GR00T-N1.6-BEHAVIOR1k**: Fine-tuned on BEHAVIOR-1K for Galaxea R1 Pro
* **GR00T-N1.6-G1-PnPAppleToPlate**: Fine-tuned for Unitree G1 loco-manipulation
* **GR00T-N1.6-DROID**: Fine-tuned for DROID robot
## Next steps
Learn about supported robot embodiments
Configure your robot's data processing
Understand the LeRobot v2 data format
Start fine-tuning on your robot