> ## Documentation Index
> Fetch the complete documentation index at: https://mintlify.com/huggingface/lerobot/llms.txt
> Use this file to discover all available pages before exploring further.

# Isaaclab arena

# NVIDIA IsaacLab Arena

IsaacLab Arena provides GPU-accelerated, high-fidelity humanoid manipulation environments for training and evaluating vision-language-action models at scale.

<img src="https://huggingface.co/nvidia/isaaclab-arena-envs/resolve/main/assets/Gr1OpenMicrowaveEnvironment.png" alt="IsaacLab Arena - GR1 Microwave Environment" style={{ maxWidth: "100%", borderRadius: "8px", marginBottom: "1rem" }} />

**Key Features**:

* 🤖 **Humanoid embodiments**: GR1, G1, Galileo with various configurations

* 🎯 **Manipulation & loco-manipulation**: Door opening, pick-and-place, button pressing

* ⚡ **GPU-accelerated rollouts**: Massively parallel environment execution

* 🖼️ **RTX Rendering**: Realistic rendering with reflections and refractions

* 📦 **LeRobot-compatible**: Ready for training with GR00T, PI0, SmolVLA, ACT, Diffusion policies

* 🔄 **EnvHub integration**: One-line environment loading

* 📄 [IsaacLab Arena GitHub](https://github.com/isaac-sim/IsaacLab-Arena)

* 📚 [IsaacLab Documentation](https://isaac-sim.github.io/IsaacLab/)

## Installation

### Prerequisites

Hardware requirements (see [Isaac Sim Requirements](https://docs.isaacsim.omniverse.nvidia.com/5.1.0/installation/requirements.html)):

* NVIDIA GPU with CUDA support
* NVIDIA driver compatible with IsaacSim 5.1.0
* Linux (Ubuntu 22.04 / 24.04)

### Setup Instructions

```bash theme={null}
# 1. Create conda environment
conda create -y -n lerobot-arena python=3.11
conda activate lerobot-arena
conda install -y -c conda-forge ffmpeg=7.1.1

# 2. Install Isaac Sim 5.1.0
pip install "isaacsim[all,extscache]==5.1.0" --extra-index-url https://pypi.nvidia.com

# Accept NVIDIA EULA (required)
export ACCEPT_EULA=Y
export PRIVACY_CONSENT=Y

# 3. Install IsaacLab 2.3.0
git clone https://github.com/isaac-sim/IsaacLab.git
cd IsaacLab
git checkout v2.3.0
./isaaclab.sh -i
cd ..

# 4. Install IsaacLab Arena
git clone https://github.com/isaac-sim/IsaacLab-Arena.git
cd IsaacLab-Arena
git checkout release/0.1.1
pip install -e .
cd ..

# 5. Install LeRobot
git clone https://github.com/huggingface/lerobot.git
cd lerobot
pip install -e .
cd ..

# 6. Install additional dependencies
pip install onnxruntime==1.23.2 lightwheel-sdk==1.0.1 vuer[all]==0.0.70 qpsolvers==4.8.1
pip install numpy==1.26.0  # Isaac Sim 5.1 requires numpy 1.26.0
```

## Pre-trained Policies

NVIDIA provides trained policies for evaluation:

| Policy                      | Architecture | Task          | Link                                                                     |
| :-------------------------- | :----------- | :------------ | :----------------------------------------------------------------------- |
| pi05-arena-gr1-microwave    | PI0.5        | GR1 Microwave | [HuggingFace](https://huggingface.co/nvidia/pi05-arena-gr1-microwave)    |
| smolvla-arena-gr1-microwave | SmolVLA      | GR1 Microwave | [HuggingFace](https://huggingface.co/nvidia/smolvla-arena-gr1-microwave) |

## Evaluating Policies

### Evaluate SmolVLA

First, install SmolVLA dependencies:

```bash theme={null}
pip install -e ".[smolvla]"
pip install numpy==1.26.0  # revert numpy to version 1.26
```

Run evaluation:

```bash theme={null}
lerobot-eval \
    --policy.path=nvidia/smolvla-arena-gr1-microwave \
    --env.type=isaaclab_arena \
    --env.hub_path=nvidia/isaaclab-arena-envs \
    --rename_map='{"observation.images.robot_pov_cam_rgb": "observation.images.robot_pov_cam"}' \
    --policy.device=cuda \
    --env.environment=gr1_microwave \
    --env.embodiment=gr1_pink \
    --env.object=mustard_bottle \
    --env.headless=false \
    --env.enable_cameras=true \
    --env.video=true \
    --env.video_length=10 \
    --env.video_interval=15 \
    --env.state_keys=robot_joint_pos \
    --env.camera_keys=robot_pov_cam_rgb \
    --trust_remote_code=True \
    --eval.batch_size=1
```

### Evaluate PI0.5

Install PI0.5 dependencies:

```bash theme={null}
pip install -e ".[pi]"
pip install numpy==1.26.0  # revert numpy to version 1.26
```

<Tip>PI0.5 requires disabling torch compile for evaluation:</Tip>

Run evaluation:

```bash theme={null}
TORCH_COMPILE_DISABLE=1 TORCHINDUCTOR_DISABLE=1 lerobot-eval \
    --policy.path=nvidia/pi05-arena-gr1-microwave \
    --env.type=isaaclab_arena \
    --env.hub_path=nvidia/isaaclab-arena-envs \
    --rename_map='{"observation.images.robot_pov_cam_rgb": "observation.images.robot_pov_cam"}' \
    --policy.device=cuda \
    --env.environment=gr1_microwave \
    --env.embodiment=gr1_pink \
    --env.object=mustard_bottle \
    --env.headless=false \
    --env.enable_cameras=true \
    --env.video=true \
    --env.video_length=15 \
    --env.video_interval=15 \
    --env.state_keys=robot_joint_pos \
    --env.camera_keys=robot_pov_cam_rgb \
    --trust_remote_code=True \
    --eval.batch_size=1
```

<Tip>
  To change the number of parallel environments, use `--eval.batch_size`.
</Tip>

### Expected Output

During evaluation, you'll see a progress bar with running success rate:

```text theme={null}
Stepping through eval batches:   8%|██████▍    | 4/50 [00:45<08:06, 10.58s/it, running_success_rate=25.0%]
```

## Training Policies

IsaacLab Arena datasets are available for training:

| Dataset                                                                                                   | Description                | Frames |
| :-------------------------------------------------------------------------------------------------------- | :------------------------- | :----- |
| [Arena-GR1-Manipulation-Task](https://huggingface.co/datasets/nvidia/Arena-GR1-Manipulation-Task-v3)      | GR1 microwave manipulation | \~4K   |
| [Arena-G1-Loco-Manipulation-Task](https://huggingface.co/datasets/nvidia/Arena-G1-Loco-Manipulation-Task) | G1 loco-manipulation       | \~4K   |

Training example:

```bash theme={null}
lerobot-train \
    --policy.type=smolvla \
    --policy.repo_id=${HF_USER}/arena-gr1-microwave \
    --dataset.repo_id=nvidia/Arena-GR1-Manipulation-Task-v3 \
    --env.type=isaaclab_arena \
    --env.hub_path=nvidia/isaaclab-arena-envs \
    --env.environment=gr1_microwave \
    --env.embodiment=gr1_pink \
    --env.object=mustard_bottle \
    --steps=50000 \
    --batch_size=8 \
    --eval_freq=5000 \
    --trust_remote_code=True
```

For policy-specific training guides:

* [SmolVLA Training](/policies/smolvla)
* [PI0.5 Training](/policies/pi05)
* [GR00T N1.5 Training](/policies/groot)

## Environment Configuration

### Full Configuration Options

```python theme={null}
from lerobot.envs.configs import IsaaclabArenaEnv

config = IsaaclabArenaEnv(
    # Environment selection
    environment="gr1_microwave",       # Task environment
    embodiment="gr1_pink",             # Robot embodiment
    object="power_drill",              # Object to manipulate
    
    # Simulation settings
    episode_length=300,                # Max steps per episode
    headless=True,                     # Run without GUI
    device="cuda:0",                   # GPU device
    seed=42,                           # Random seed
    
    # Observation configuration
    state_keys="robot_joint_pos",      # State observation keys (comma-separated)
    camera_keys="robot_pov_cam_rgb",   # Camera observation keys (comma-separated)
    state_dim=54,                      # Expected state dimension
    action_dim=36,                     # Expected action dimension
    camera_height=512,                 # Camera image height
    camera_width=512,                  # Camera image width
    enable_cameras=True,               # Enable camera observations
    
    # Video recording
    video=False,                       # Enable video recording
    video_length=100,                  # Frames per video
    video_interval=200,                # Steps between recordings
    
    # Advanced
    mimic=False,                       # Enable mimic mode
    teleop_device=None,                # Teleoperation device
    disable_fabric=False,              # Disable fabric optimization
    enable_pinocchio=True,             # Enable Pinocchio for IK
)
```

### Using EnvHub Directly

For advanced usage, load environments directly:

```python theme={null}
# test_env_load_arena.py
import logging
from dataclasses import asdict
from pprint import pformat
import torch
import tqdm
from lerobot.configs import parser
from lerobot.configs.eval import EvalPipelineConfig


@parser.wrap()
def main(cfg: EvalPipelineConfig):
    """Run random action rollout for IsaacLab Arena environment."""
    logging.info(pformat(asdict(cfg)))
    
    from lerobot.envs.factory import make_env
    
    env_dict = make_env(
        cfg.env,
        n_envs=cfg.env.num_envs,
        trust_remote_code=True,
    )
    env = next(iter(env_dict.values()))[0]
    env.reset()
    
    for _ in tqdm.tqdm(range(cfg.env.episode_length)):
        with torch.inference_mode():
            actions = env.action_space.sample()
            obs, rewards, terminated, truncated, info = env.step(actions)
            if terminated.any() or truncated.any():
                obs, info = env.reset()
    
    env.close()


if __name__ == "__main__":
    main()
```

Run with:

```bash theme={null}
python test_env_load_arena.py \
    --env.environment=g1_locomanip_pnp \
    --env.embodiment=gr1_pink \
    --env.object=cracker_box \
    --env.num_envs=4 \
    --env.enable_cameras=true \
    --env.seed=1000 \
    --env.video=true \
    --env.video_length=10 \
    --env.video_interval=15 \
    --env.headless=false \
    --env.hub_path=nvidia/isaaclab-arena-envs \
    --env.type=isaaclab_arena
```

## Video Recording

Enable video recording during evaluation:

```bash theme={null}
--env.video=true \
--env.video_length=15 \
--env.video_interval=15
```

<Tip>
  When running headless, explicitly enable cameras:

  ```bash theme={null}
  --env.headless=true --env.enable_cameras=true
  ```
</Tip>

### Output Directory

Videos are saved to:

```text theme={null}
outputs/eval/<date>/<timestamp>_<env>_<policy>/videos/<task>_<env_id>/eval_episode_<n>.mp4
```

Example:

```text theme={null}
outputs/eval/2026-01-02/14-38-01_isaaclab_arena_smolvla/videos/gr1_microwave_0/eval_episode_0.mp4
```

See [IsaacLab Recording Documentation](https://isaac-sim.github.io/IsaacLab/main/source/how-to/record_video.html) for details.

## Creating New Environments

1. **Create IsaacLab Arena environment**: Follow [IsaacLab Arena Documentation](https://isaac-sim.github.io/IsaacLab-Arena/release/0.1.1/index.html)

2. **Clone EnvHub repo**:
   ```bash theme={null}
   git clone https://huggingface.co/nvidia/isaaclab-arena-envs
   ```

3. **Modify `example_envs.yaml`** based on your environment

4. **Upload to EnvHub**: See [EnvHub guide](./envhub)

<Tip>
  Your IsaacLab Arena environment code must be locally available during
  evaluation. Either clone separately or bundle in your EnvHub repo.
</Tip>

5. **Evaluate with your environment**:
   ```bash theme={null}
   lerobot-eval \
       --env.hub_path=<your-username>/isaaclab-arena-envs \
       --env.environment=<your-new-environment> \
       ...other flags...
   ```

## Lightwheel LW-BenchHub

[Lightwheel](https://www.lightwheel.ai) provides **268 tasks** across LIBERO and RoboCasa with large-scale datasets:

### Installation

```bash theme={null}
conda install pinocchio -c conda-forge -y
pip install numpy==1.26.0

sudo apt-get install git-lfs && git lfs install

git clone https://github.com/LightwheelAI/lw_benchhub
git lfs pull  # Download .usd assets

cd lw_benchhub
pip install -e .
```

See [LW-BenchHub Documentation](https://docs.lightwheel.net/lw_benchhub/usage/Installation) for details.

### Datasets

| Dataset                                                                                                       | Description             | Tasks | Frames  |
| :------------------------------------------------------------------------------------------------------------ | :---------------------- | :---- | :------ |
| [Lightwheel-Tasks-X7S](https://huggingface.co/datasets/LightwheelAI/Lightwheel-Tasks-X7S)                     | X7S LIBERO and RoboCasa | 117   | \~10.3M |
| [Lightwheel-Tasks-Double-Piper](https://huggingface.co/datasets/LightwheelAI/Lightwheel-Tasks-Double-Piper)   | Double-Piper LIBERO     | 130   | \~6.0M  |
| [Lightwheel-Tasks-G1-Controller](https://huggingface.co/datasets/LightwheelAI/Lightwheel-Tasks-G1-Controller) | G1-Controller LIBERO    | 62    | \~2.7M  |
| [Lightwheel-Tasks-G1-WBC](https://huggingface.co/datasets/LightwheelAI/Lightwheel-Tasks-G1-WBC)               | G1-WBC RoboCasa         | 32    | \~1.5M  |

### Pre-trained Policies

| Policy                   | Architecture | Task                           | Layout     | Robot           | Link                                                                        |
| :----------------------- | :----------- | :----------------------------- | :--------- | :-------------- | :-------------------------------------------------------------------------- |
| smolvla-double-piper-pnp | SmolVLA      | L90K1PutTheBlackBowlOnThePlate | libero-1-1 | DoublePiper-Abs | [HuggingFace](https://huggingface.co/LightwheelAI/smolvla-double-piper-pnp) |

### Evaluate SmolVLA on LW-BenchHub

```bash theme={null}
lerobot-eval \
    --policy.path=LightwheelAI/smolvla-double-piper-pnp \
    --env.type=isaaclab_arena \
    --rename_map='{"observation.images.left_hand_camera_rgb": "observation.images.left_hand", "observation.images.right_hand_camera_rgb": "observation.images.right_hand", "observation.images.first_person_camera_rgb": "observation.images.first_person"}' \
    --env.hub_path=LightwheelAI/lw_benchhub_env \
    --env.kwargs='{"config_path": "configs/envhub/example.yml"}' \
    --trust_remote_code=true \
    --env.state_keys=joint_pos \
    --env.action_dim=12 \
    --env.camera_keys=left_hand_camera_rgb,right_hand_camera_rgb,first_person_camera_rgb \
    --policy.device=cuda \
    --eval.batch_size=10 \
    --eval.n_episodes=100
```

## Troubleshooting

### CUDA out of memory

Reduce batch size:

```bash theme={null}
--eval.batch_size=1
```

### EULA not accepted

Set environment variables:

```bash theme={null}
export ACCEPT_EULA=Y
export PRIVACY_CONSENT=Y
```

### Video recording not working

Enable cameras when running headless:

```bash theme={null}
--env.video=true --env.enable_cameras=true --env.headless=true
```

### Policy output dimension mismatch

Ensure `action_dim` matches your policy:

```bash theme={null}
--env.action_dim=36
```

### libGLU.so.1 Errors

Install missing dependencies:

```bash theme={null}
sudo apt update && sudo apt install -y libglu1-mesa libxt6
```

## See Also

* [EnvHub Documentation](./envhub): General EnvHub usage
* [IsaacLab Arena GitHub](https://github.com/isaac-sim/IsaacLab-Arena)
* [IsaacLab Documentation](https://isaac-sim.github.io/IsaacLab/)
* [LW-BenchHub](https://github.com/LightwheelAI/LW-BenchHub)