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.

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.

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:

pip install "lerobot[groot]"

For a source checkout:

pip install -e ".[groot]"

Usage

To use GR00T N1.7:

--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..

# 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

GR00T N1.7 has demonstrated strong performance on the LIBERO benchmark suite. To reproduce LeRobot results, follow the instructions in the LIBERO section.

Train on LIBERO

Example training command for a LIBERO suite (here libero_spatial):

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.

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
LIBERO Object	81%	nvidia/gr00t17-lerobot-libero_object-640
LIBERO Goal	97%	nvidia/gr00t17-lerobot-libero_goal-640
LIBERO 10 (Long)	84%	nvidia/gr00t17-lerobot-libero_10-640
Average	88.25%

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). For example:

# 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

License

GR00T N1.7 is released under the NVIDIA Open Model License Agreement.