NVIDIA Releases GR00T N1.7 Model for Generalized Humanoid Robot Reasoning and Skills
nvidia/gr00t17-lerobot-libero_goal-640
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 for training and deployment under the Apache 2.0 license.
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Key findings, evidence and limits
Published automatically after robotics and source-evidence checks; no manual editorial approval is recorded. Source assertions are not independently verified. Missing information remains not reported.
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Source excerpts and review record
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Original source quotation: “Model Card for groot GR00T N1.7 is an open, cross-embodiment foundation model from NVIDIA for generalized humanoid robot reasoning and skills.”
Source E1
Original source quotation: “It uses a Cosmos-Reason2/Qwen3-VL backbone and a flow-matching action transformer to predict actions conditioned on vision, language, and proprioception.”
Source E2
Original source quotation: “This policy has been trained and pushed to the Hub using LeRobot .”
Source E3
Original source quotation: “Model Details License: apache-2.0 Inputs & Outputs The policy consumes these observation features and produces these action features.”
Source E4
Original source quotation: “Training steps 20000 Batch size 320 Optimizer adamw Learning rate 0.0001 Seed 42 LeRobot version 0.6.1”
Source E5
Original source quotation: “How to Get Started with the Model New to LeRobot? These guides cover the full workflow: Install LeRobot — set up the lerobot package.”
Source E6
Original source quotation: “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_goal-640 \ --task= "<your_task_description>" \ --duration=60”
Source E7
Original source quotation: “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.”
Source E8
Original source quotation: “When --strategy.type=base is used the script doesn't record the episodes. Skipping duration will make the policy run indefinitely.”
Source E9
Original source quotation: “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/ .”
Source E10
Original source quotation: “Evaluation No evaluation results have been provided for this policy yet.”
Source E11
Original source quotation: “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} }”
Source E12
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Source：NVIDIA — Robotics model repository updates · huggingface.co