Model Card for groot
GR00T N1.7 is an open, cross-embodiment foundation model from NVIDIA for generalized humanoid robot reasoning and skills. It uses a Cosmos-Reason2/Qwen3-VL backbone and a flow-matching action transformer to predict actions conditioned on vision, language, and proprioception.
This policy has been trained and pushed to the Hub using LeRobot.
Learn how to train and run it in the LeRobot groot guide, or browse the full documentation.
Model Details
- License: apache-2.0
- Robot type:
so_follower
- Cameras:
overhead, wrist
Inputs & Outputs
The policy consumes these observation features and produces these action features.
Inputs
| Feature |
Type |
Shape |
observation.state |
STATE |
(6,) |
observation.images.overhead |
VISUAL |
(3, 480, 640) |
observation.images.wrist |
VISUAL |
(3, 480, 640) |
Outputs
| Feature |
Type |
Shape |
action |
ACTION |
(6,) |
Training Dataset
- Repository: yudai0209/so101_cvs2_80ep
- Episodes: 80
- Frames: 47379
- Frame rate: 30 FPS
- Task(s): "Pick up the Black Thunder and place it on the mark.", "Pick up the madeleine and place it on the mark."
Training Configuration
| Setting |
Value |
| Training steps |
20000 |
| Batch size |
32 |
| Optimizer |
adamw |
| Learning rate |
0.0001 |
| Seed |
42 |
| LeRobot version |
0.6.1 |
How to Get Started with the Model
New to LeRobot? These guides cover the full workflow:
The short version to run and train this policy:
Run the policy on your robot
lerobot-rollout \
--strategy.type=base \
--robot.type=so_follower \
--robot.port=<your_robot_port> \
--robot.cameras="{ <camera_1>: {type: opencv, index_or_path: <index_or_path>, width: 640, height: 480, fps: 30}, <camera_2>: {type: opencv, index_or_path: <index_or_path>, width: 640, height: 480, fps: 30}}" \
--policy.path=yudai0209/so101_cvs2_80ep_groot17 \
--task="Pick up the Black Thunder and place it on the mark." \
--duration=60
Replace the remaining <...> placeholders with your own values: --robot.port and the camera names/indices are specific to your machine, and the camera names must match the observation keys this policy was trained on.
When --strategy.type=base is used the script doesn't record the episodes. Skipping duration will make the policy run indefinitely. For more information look at rollout documentation.
Train your own policy
lerobot-train \
--dataset.repo_id=${HF_USER}/<dataset> \
--policy.type=groot \
--output_dir=outputs/train/<policy_repo_id> \
--job_name=lerobot_training \
--policy.device=cuda \
--policy.repo_id=${HF_USER}/<policy_repo_id> \
--wandb.enable=true
Writes checkpoints to outputs/train/<policy_repo_id>/checkpoints/.
Evaluation
No evaluation results have been provided for this policy yet.
Citation
If you use this policy, please cite the method linked in the description above, along with LeRobot:
@misc{cadene2024lerobot,
author = {Cadene, Remi and Alibert, Simon and Soare, Alexander and Gallouedec, Quentin and Zouitine, Adil and Palma, Steven and Kooijmans, Pepijn and Aractingi, Michel and Shukor, Mustafa and Aubakirova, Dana and Russi, Martino and Capuano, Francesco and Pascal, Caroline and Choghari, Jade and Moss, Jess and Wolf, Thomas},
title = {LeRobot: State-of-the-art Machine Learning for Real-World Robotics in Pytorch},
howpublished = "\url{https://github.com/huggingface/lerobot}",
year = {2024}
}
