NVIDIA Releases Cross-Embodiment Foundation Model for Humanoid Robots

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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

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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_spatial-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.

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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