# GR00T Policy GR00T is an NVIDIA foundation model family for generalized humanoid robot reasoning and skills. It is a cross-embodiment policy that accepts multimodal input, including language, images, and proprioception, to perform manipulation tasks in diverse environments. LeRobot integrates GR00T N1.7 through the `groot` policy type. > [!WARNING] > **Breaking change:** GR00T N1.5 support was removed from LeRobot, and current releases support GR00T N1.7 only. N1.5 checkpoints and configs are rejected with a migration note. To keep using an N1.5 checkpoint, pin the last release that supports it: `pip install 'lerobot==0.5.1'`. To use the current release, migrate to GR00T N1.7 (base model [`nvidia/GR00T-N1.7-3B`](https://huggingface.co/nvidia/GR00T-N1.7-3B)). ## Model Overview GR00T N1.7 uses a Cosmos-Reason2/Qwen3-VL backbone and provides checkpoints for SimplerEnv, DROID, and LIBERO. Developers and researchers can post-train GR00T with their own real or synthetic data to adapt it for specific humanoid robots or tasks. GR00T uses pre-trained vision and language encoders with a flow matching action transformer to model a chunk of actions conditioned on vision, language, and proprioception. An overview of GR00T Its strong performance comes from being trained on an expansive and diverse humanoid dataset, which includes: - Real captured data from robots. - Synthetic data generated using NVIDIA Isaac GR00T Blueprint. - Internet-scale video data. This approach allows the model to be highly adaptable through post-training for specific embodiments, tasks, and environments. ## Installation Requirements GR00T is intended for NVIDIA GPU-accelerated systems. Install LeRobot with the GR00T extra: ```bash pip install "lerobot[groot]" ``` For a source checkout: ```bash pip install -e ".[groot]" ``` ## Usage To use GR00T N1.7: ```bash --policy.type=groot ``` ## Training ### Training Command Example Here's a complete training command for finetuning the base GR00T model on your own dataset: This command is using the `new_embodiment` flag, which is used for the SO-101 robot, [read more about how GR00T handles different embodiments.](https://github.com/NVIDIA/Isaac-GR00T/blob/main/getting_started/policy.md#--embodiment-tag). ```bash # install extra deps for training pip install "lerobot[training]" hf auth login wandb login export DATASET_NAME=your_data_set export HF_USER=your_hf_username export DATASET=$HF_USER/$DATASET_NAME export REPO_ID="${DATASET}_GR00T17" #this is the model that will be uploaded to huggingface export OUTPUT_DIR=outputs/train/$REPO_ID lerobot-train \ --dataset.repo_id=$DATASET \ --dataset.image_transforms.enable=true \ --policy.type=groot \ --policy.device=cuda \ --policy.base_model_path=nvidia/GR00T-N1.7-3B \ --policy.embodiment_tag=new_embodiment \ --policy.chunk_size=16 \ --policy.n_action_steps=16 \ --policy.use_relative_actions=true \ --policy.relative_exclude_joints='["gripper"]' \ --policy.use_bf16=true \ --policy.push_to_hub=true \ --policy.repo_id=$REPO_ID \ --seed=42 \ --batch_size=64 \ --steps=20000 \ --save_checkpoint=true \ --save_freq=5000 \ --use_policy_training_preset=true \ --env_eval_freq=0 \ --eval_steps=0 \ --log_freq=10 \ --output_dir=$OUTPUT_DIR \ --job_name=$DATASET \ --wandb.enable=true \ --wandb.disable_artifact=true ``` ## Performance Results ### LIBERO Benchmark Results > [!NOTE] > Follow the [LIBERO](./libero) setup instructions before running `lerobot-eval`. GR00T N1.7 has demonstrated strong performance on the LIBERO benchmark suite. To reproduce LeRobot results, follow the instructions in the [LIBERO](./libero) section. ### Train on LIBERO Example training command for a LIBERO suite (here `libero_spatial`): ```bash IMAGE_TRANSFORMS='{ "brightness": {"weight": 1.0, "type": "ColorJitter", "kwargs": {"brightness": [0.7, 1.3]}}, "contrast": {"weight": 1.0, "type": "ColorJitter", "kwargs": {"contrast": [0.6, 1.4]}}, "saturation": {"weight": 1.0, "type": "ColorJitter", "kwargs": {"saturation": [0.5, 1.5]}}, "hue": {"weight": 1.0, "type": "ColorJitter", "kwargs": {"hue": [-0.08, 0.08]}} }' lerobot-train \ --dataset.repo_id=IPEC-COMMUNITY/libero_spatial_no_noops_1.0.0_lerobot \ --dataset.root=/datasets/libero_spatial \ --dataset.revision=main \ --dataset.video_backend=pyav \ --dataset.image_transforms.enable=true \ --dataset.image_transforms.max_num_transforms=4 \ --dataset.image_transforms.tfs="$IMAGE_TRANSFORMS" \ --policy.type=groot \ --policy.base_model_path=nvidia/GR00T-N1.7-3B \ --policy.embodiment_tag=libero_sim \ --policy.push_to_hub=false \ --policy.use_relative_actions=false \ --policy.max_steps=20000 \ --batch_size=320 \ --steps=20000 \ --save_freq=2000 \ --env_eval_freq=0 \ --eval_steps=0 \ --log_freq=10 \ --wandb.enable=true \ --wandb.project=lerobot \ --wandb.mode=online \ --wandb.disable_artifact=true \ --num_workers=4 \ --prefetch_factor=2 \ --persistent_workers=true \ --output_dir=$OUTPUT_DIR \ --job_name=$JOB_NAME ``` This will follow the recipe found [here](https://github.com/NVIDIA/Isaac-GR00T/blob/main/examples/LIBERO/README.md). ### GR00T N1.7 LIBERO Results Preliminary LeRobot integration results (GR00T-LeRobot, `eval.n_episodes >= 50` per suite): | Suite | Success rate | Checkpoint | | ---------------- | -----------: | ------------------------------------------------------------------------------------------------------------- | | LIBERO Spatial | 91% | [nvidia/gr00t17-lerobot-libero_spatial-640](https://huggingface.co/nvidia/gr00t17-lerobot-libero_spatial-640) | | LIBERO Object | 81% | [nvidia/gr00t17-lerobot-libero_object-640](https://huggingface.co/nvidia/gr00t17-lerobot-libero_object-640) | | LIBERO Goal | 97% | [nvidia/gr00t17-lerobot-libero_goal-640](https://huggingface.co/nvidia/gr00t17-lerobot-libero_goal-640) | | LIBERO 10 (Long) | 84% | [nvidia/gr00t17-lerobot-libero_10-640](https://huggingface.co/nvidia/gr00t17-lerobot-libero_10-640) | | **Average** | **88.25%** | | ```bash export MODEL_ID=your_trained_model_on_huggingface lerobot-eval \ --policy.type=groot \ --policy.base_model_path=$MODEL_ID \ --policy.embodiment_tag=libero_sim \ --env.type=libero \ --env.task=libero_spatial \ --eval.n_episodes=50 ``` Use `eval.n_episodes >= 50` per suite when reporting success rates. ### Evaluate in your hardware setup Once you have trained your model using your parameters you can run inference in your downstream task. Follow the instructions in [Policy Deployment (lerobot-rollout)](./inference). For example: ```bash # install extra deps for roullout and real hardware pip install "lerobot[feetech,viz]" export MODEL_ID=your_trained_model_on_huggingface # make sure that camera index matches your setup! # find index using `uv run lerobot-find-cameras opencv` WRIST_CAM='wrist: {type: opencv, index_or_path: 2, width: 640, height: 480, fps: 30, fourcc: "MJPG"}' FRONT_CAM='front: {type: opencv, index_or_path: 0, width: 640, height: 480, fps: 30, fourcc: "MJPG"}' export ROBOT_CAMERAS="{ $WRIST_CAM, $FRONT_CAM }" export ROBOT_ID=follower_robot export ROBOT_PORT=/dev/ttyACM0 uv run lerobot-rollout \ --strategy.type=base \ --policy.path=$MODEL_ID \ --policy.base_model_path=nvidia/GR00T-N1.7-3B \ --policy.n_action_steps=8 \ --robot.type=so101_follower \ --robot.port=$ROBOT_PORT \ --robot.id=$ROBOT_ID \ --robot.cameras="$ROBOT_CAMERAS" \ --task="place the vial in the rack" \ --duration=60 \ --device=cuda \ --display_data=true \ --inference.type=rtc \ --inference.rtc.enabled=True \ # set to False if it causes inference instability --inference.rtc.execution_horizon=8 \ --inference.queue_threshold=0 ``` > [!NOTE] > Value of `inference.queue_threshold` should not exceed 5 to ensure stable inference. ## License GR00T N1.7 is released under the [NVIDIA Open Model License Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/).