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Cataloged from nvidia/gr00t17-lerobot-libero_spatial-640
All models by nvidia →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.
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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.
The policy consumes these observation features and produces these action features.
Inputs
Outputs
New to LeRobot? These guides cover the full workflow:
lerobot package.lerobot-* commands.The short version to run and train this policy:
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.
Writes checkpoints to outputs/train/<policy_repo_id>/checkpoints/.
No evaluation results have been provided for this policy yet.
If you use this policy, please cite the method linked in the description above, along with LeRobot:
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=60lerobot-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@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}}