lerobot/docs/source/groot.mdx

# 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.

<img
  src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/lerobot-groot-paper1%20(1).png"
  alt="An overview of GR00T"
  width="80%"
/>

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]"
```

### Optional: Flash Attention acceleration

Flash Attention is a purely optional performance optimization. **LeRobot neither installs nor requires it**, and setting it up is up to the user as it has environment-specific build requirements (a matching PyTorch/CUDA toolchain). To enable it:

1. Install a `flash-attn` build matching your PyTorch/CUDA environment (see the [Flash Attention project](https://github.com/Dao-AILab/flash-attention)):

```bash
# Check https://pytorch.org/get-started/locally/ for the right CUDA wheel index for your system.
pip install "torch>=2.7,<2.12.0" "torchvision>=0.22.0,<0.27.0" \
  --index-url https://download.pytorch.org/whl/cu128
pip install "ninja>=1.11.1,<2.0.0" "packaging>=24.2,<26.0"
pip install "flash-attn>=2.5.9,<3.0.0" --no-build-isolation
python -c "import flash_attn; print(f'Flash Attention {flash_attn.__version__} imported successfully')"
```

2. Install lerobot with the groot extra.

3. Opt in by passing `--policy.use_flash_attention=true` when training/evaluating GR00T. If the kernel is missing or fails to import, the backbone transparently falls back to SDPA.

## 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:

```bash
export BASE_MODEL=nvidia/GR00T-N1.7-3B

uv run lerobot-train \
  --dataset.repo_id=sreetz-nv/so101-clean-up-vials-into-rack-50_20260628_131121 \
  --dataset.image_transforms.enable=true \
  --policy.type=groot \
  --policy.device=cuda \
  --policy.base_model_path=$BASE_MODEL \
  --policy.embodiment_tag=new_embodiment \
  --policy.chunk_size=16 \
  --policy.n_action_steps=16 \
  --policy.use_relative_actions=true \
  --policy.relative_exclude_joints='["gripper"]' \
  --policy.use_bf16=true \
  --policy.use_flash_attention=true \
  --policy.push_to_hub=true \
  --policy.repo_id=sreetz-nv/so101-clean-up-vials-into-rack-50-groot-n17-relact-bs64-20k-20260628_johnny_42 \
  --seed=42 \
  --batch_size=64 \
  --steps=20000 \
  --save_checkpoint=true \
  --use_policy_training_preset=true \
  --env_eval_freq=0 \
  --eval_steps=0 \
  --log_freq=100 \
  --output_dir=$OUTPUT_DIR \
  --job_name=$JOB_NAME
```

## 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.

### GR00T N1.7 LIBERO Checkpoints

NVIDIA publishes GR00T N1.7 LIBERO checkpoints at [`nvidia/GR00T-N1.7-LIBERO`](https://huggingface.co/nvidia/GR00T-N1.7-LIBERO), with one subdirectory per LIBERO suite:

| Suite          | Checkpoint subdirectory |
| -------------- | ----------------------- |
| LIBERO Spatial | `libero_spatial`        |
| LIBERO Object  | `libero_object`         |
| LIBERO Goal    | `libero_goal`           |
| LIBERO 10      | `libero_10`             |

Preliminary LeRobot integration results (GR00T-LeRobot, `eval.n_episodes >= 50` per suite):

| Suite                  | Success rate |
| ---------------------- | -----------: |
| LIBERO Spatial         |          94% |
| LIBERO Object          |          98% |
| LIBERO Goal            |          93% |
| LIBERO 10 (Long)       |          90% |
| **Average**            |   **93.75%** |

Download the suite checkpoint locally, then point `--policy.base_model_path` at the downloaded subdirectory. `--policy.path` is reserved for LeRobot checkpoints that contain a LeRobot `config.json` with a `type` field.

```bash
hf download nvidia/GR00T-N1.7-LIBERO \
  --include "libero_spatial/*" \
  --local-dir ./GR00T-N1.7-LIBERO

lerobot-eval \
  --policy.type=groot \
  --policy.base_model_path=./GR00T-N1.7-LIBERO/libero_spatial \
  --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
uv run lerobot-rollout \
  --strategy.type=base \
  --inference.type=rtc \
  --policy.path=$OUTPUT_DIR/checkpoints/020000/pretrained_model/ \
  --policy.base_model_path=$BASE_MODEL \
  --robot.type=so101_follower \
  --robot.port=/dev/ttyACM0 \
  --robot.id=orange_andrew \
  --robot.cameras='{ wrist: {type: opencv, index_or_path: 0, width: 640, height: 480, fps: 30, fourcc: "MJPG"}, front: {type: opencv, index_or_path: 2, width: 640, height: 480, fps: 30, fourcc: "MJPG"} }' \
  --task="place the vial in the rack" \
  --duration=60 \
  --device=cuda \
  --display_data=true \
  --inference.rtc.enabled=false \
  --inference.rtc.execution_horizon=8 \
  --inference.queue_threshold=0 \
  --policy.n_action_steps=8
```

## 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/).