NVIDIA Releases Cross-Embodiment Foundation Model for Humanoid Robots
nvidia/gr00t17-lerobot-libero_object-640
NVIDIA has released the 'gr00t17-lerobot-libero_object-640' model, a cross-embodiment foundation model for humanoid robot reasoning and skills. It uses a Cosmos-Reason2/Qwen3-VL backbone and a flow-matching action transformer, trained with LeRobot and available under the Apache 2.0 license. The model supports training and deployment through LeRobot, with detailed instructions provided.
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NVIDIA has released a cross-embodiment foundation model for humanoid robots, leveraging a Cosmos-Reason2/Qwen3-VL backbone and a flow-matching action transformer. The model is trained using LeRobot and is available under the Apache 2.0 license, with instructions for training and deployment provided in the documentation.
What the source reports
Publisher-reported claims, with original evidence. These results have not been independently verified by RoboSignal.
- dataset: not_reported
What remains unknown
Not established in the collected evidence: Environment, Control, Data origin.
Reported performance applies to the described task. It does not establish general autonomy or deployment readiness.
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Model Card for groot GR00T N1.7 is an open, cross-embodiment foundation model from NVIDIA for generalized humanoid robot reasoning and skills.
Open source E1
It uses a Cosmos-Reason2/Qwen3-VL backbone and a flow-matching action transformer to predict actions conditioned on vision, language, and proprioception.
Open source E2
This policy has been trained and pushed to the Hub using LeRobot .
Open source E3
Model Details License: apache-2.0 Inputs & Outputs The policy consumes these observation features and produces these action features.
Open source E4
Training steps 20000 Batch size 320 Optimizer adamw Learning rate 0.0001 Seed 42 LeRobot version 0.6.1
Open source E5
How to Get Started with the Model New to LeRobot? These guides cover the full workflow: Install LeRobot — set up the lerobot package.
Open source E6
The short version to run and train this policy: Run the policy on your robot lerobot-rollout \ --strategy.type=base \ --robot.type=<your_robot_type> \ --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=nvidia/gr00t17-lerobot-libero_object-640 \ --task= "<your_task_description>" \ --duration=60
Open source E7
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.
Open source E8
When --strategy.type=base is used the script doesn't record the episodes. Skipping duration will make the policy run indefinitely.
Open source E9
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/ .
Open source E10
Evaluation No evaluation results have been provided for this policy yet.
Open source E11
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} }
Open source E12
Implications for data suppliers
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Source：NVIDIA — Robotics model repository updates · huggingface.co