Model Overview
Description:
G1 Locomanipulation Finetune fine-tunes the GR00T N1.5 Vision-Language-Action (VLA) foundation model to perform loco-manipulation on the Unitree G1 humanoid robot, jointly controlling bimanual manipulation and whole-body locomotion to pick up an object at one location, navigate to a second location, and place it there. This checkpoint is provided as an example artifact for the Isaac Lab locomanipulation SDG pipeline — users who have already run the data generation and GR00T N1.5 training steps can reproduce it, but it is offered here so those steps can be skipped when working through the pipeline examples. This model is for demonstration purposes and not for production usage.
License/Terms of Use:
Governing Terms: Use of this model is governed by the
NVIDIA License.
Deployment Geography:
Global
Use Case:
This model is an example artifact for users working through the
Isaac Lab locomanipulation SDG pipeline. It demonstrates the output of running the locomanipulation data generation pipeline followed by GR00T N1.5 finetuning. It is provided so that users can skip those steps and proceed directly to running rollout/evaluation examples. It is not intended for deployment on physical robots or production systems.
Release Date:
Reference(s):
Model Architecture:
Architecture Type: Transformer (Vision-Language-Action)
Network Architecture: GR00T N1.5
This model was developed based on
nvidia/GR00T-N1.5-3B.
Number of model parameters: ~3 × 10^9 (3B)
Input(s):
Input Type(s): Video, Tabular
Input Format(s):
- Video: Red, Green, Blue (RGB), HDF5-encoded frames
- Tabular: Float32 arrays
Input Parameters:
- Video: Three-Dimensional (3D) — T × H × W
- Tabular: One-Dimensional (1D) vectors
Other Properties Related to Input:
| Modality | Description | Shape |
|---|
video.ego_view | RGB from torso-mounted Intel D435 camera | 224×224 (preprocessed) |
state.left_hand_pose | Left wrist pose (xyz + xyzw quat) | 7D |
state.right_hand_pose | Right wrist pose (xyz + xyzw quat) | 7D |
state.left_hand_joint_positions | Left finger joint angles | 7D |
state.right_hand_joint_positions | Right finger joint angles | 7D |
state.object_pose | Manipulated object pose | 7D |
state.goal_pose | Target placement pose | 7D |
state.end_fixture_pose | Drop-off table pose | 7D |
Video preprocessing: center crop 95%, resize 224×224, color jitter. State/action normalization: min-max.
Our AI models are designed and/or optimized to run on NVIDIA GPU-accelerated systems. By leveraging NVIDIA's hardware (e.g. GPU cores) and software frameworks (e.g., CUDA libraries), the model achieves faster training and inference times compared to CPU-only solutions.
Output(s):
Output Type(s): Tabular
Output Format(s):
Output Parameters:
- Tabular: One-Dimensional (1D) vectors
Other Properties Related to Output:
| Key | Description | Dim |
|---|
action.left_hand_pose | Left end-effector target (xyz + xyzw quat) | 7D |
action.right_hand_pose | Right end-effector target (xyz + xyzw quat) | 7D |
action.left_hand_joint_positions | Left finger joint targets | 7D |
action.right_hand_joint_positions | Right finger joint targets | 7D |
action.base_velocity | Base navigation command (vx, vy, yaw_rate) | 3D |
action.base_height | Base height target | 1D |
Total action dimension: 32D (28D manipulation + 4D locomotion)
Software Integration:
Runtime Engine(s):
- Not Applicable (N/A) — Isaac Lab deployment stack
Supported Hardware Microarchitecture Compatibility:
Supported Operating System(s):
The integration of foundation and fine-tuned models into AI systems requires additional testing using use-case-specific data to ensure safe and effective deployment. Following the V-model methodology, iterative testing and validation at both unit and system levels are essential to mitigate risks, meet technical and functional requirements, and ensure compliance with safety and ethical standards before deployment.
Model Version(s):
v1.0 — Initial fine-tuned checkpoint of GR00T N1.5 for G1 loco-manipulation, trained for 10,000 steps on synthetic Isaac Lab SDG demonstrations. Data config class: G1LocomanipulationSDGDataConfig.
Training and Evaluation Datasets:
Training Dataset:
Data Modality:
- Video (RGB ego-view)
- Other: Proprioceptive state — end-effector poses, finger joint positions, object/goal/fixture poses
Training Data Size:
- Video Training Data Size: Less than 10,000 Hours
- Non-Audio, Image, Text Training Data Size: 100K–1M timesteps; ~478 MB compressed
Data Collection Method by dataset:
- Synthetic — Generated via Isaac Lab SDG pipeline
Labeling Method by dataset:
- Automatic/Sensors — Action labels derived from simulation state at each timestep (200 Hz)
Properties: Synthetic robot demonstration data. Modalities: RGB video (3D, T×H×W) and proprioceptive numerical state (1D vectors). Content: machine-generated simulation data with no personal data or copyrighted content. Sensor type: simulated Intel D435 RGB camera and simulated joint encoders.
Evaluation Dataset:
Benchmark Score: Not Applicable — no quantitative benchmarks have been established for this demonstration checkpoint.
Data Collection Method by dataset:
- Synthetic — Isaac Lab simulation
Labeling Method by dataset:
Properties: Evaluation performed qualitatively in Isaac Lab simulation environment using the same synthetic SDG pipeline. No quantitative benchmark scores have been established.
Inference:
Acceleration Engine: Not Applicable (N/A)
Test Hardware:
- NVIDIA GPU (Ampere or newer recommended)
Key Considerations:
This model is provided solely as a demonstration artifact for the Isaac Lab locomanipulation SDG pipeline examples and is not intended for deployment on physical robots or production systems. It was trained entirely on synthetic simulation data and has not been validated for real-world use.
Ethical Considerations:
NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.
Please report model quality, risk, security vulnerabilities or NVIDIA AI Concerns
here.
Citation
If you use this model, please cite GR00T N1.5:
1@misc{nvidia2025groot,
2 title = {GR00T N1: An Open Foundation Model for Generalist Humanoid Robots},
3 author = {NVIDIA},
4 year = {2025},
5 url = {https://huggingface.co/nvidia/GR00T-N1.5-3B},
6}
