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:- Vision-Language Model (VLM): Cosmos-Reason-2B variant that encodes images in their native aspect ratio without padding
- Diffusion Transformer (DiT): 32-layer transformer that denoises continuous action chunks
- 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 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.
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 (3B parameters)
- GR00T N1.5: 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
Embodiment tags
Learn about supported robot embodiments
Modality configs
Configure your robot’s data processing
Data format
Understand the LeRobot v2 data format
Fine-tuning guide
Start fine-tuning on your robot