lerobot/docs/source/envhub_isaaclab_arena.mdx

# NVIDIA IsaacLab Arena & LeRobot

LeRobot EnvHub now supports **GPU-accelerated simulation** with IsaacLab Arena for policy evaluation at scale.
Train and evaluate imitation learning policies with high-fidelity simulation — all integrated into the LeRobot ecosystem.

<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" }}
/>

[IsaacLab Arena](https://github.com/isaac-sim/IsaacLab-Arena) integrates with NVIDIA IsaacLab to provide:

- 🤖 **Humanoid embodiments**: GR1, G1, Galileo with various configurations
- 🎯 **Manipulation & loco-manipulation tasks**: Microwave opening, pick-and-place, button pressing
- ⚡ **GPU-accelerated rollouts**: Parallel environment execution on NVIDIA GPUs
- 🖼️ **RTX Rendering**: Evaluate vision-based policies with realistic rendering, reflections and refractions.
- 📦 **LeRobot-compatible datasets**: Ready for training with GR00T Nx, PI0, SmolVLA, ACT, Diffusion policies
- 🔄 **EnvHub integration**: Load environments from HuggingFace Hub with one line

## Installation

### Prerequisites

Hardware requirements are shared with Isaac Sim, and are detailed in [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

```bash
# 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 depends on numpy==1.26.0, this will be fixed in next release
```

## Evaluating Policies

### Pre-trained Policies

The following trained policies are available:

| 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) |

### Evaluate SmolVLA

```bash
pip install -e ".[smolvla]"
pip install numpy==1.26.0 # revert to numpy version is 1.26
```

```bash
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

```bash
pip install -e ".[pi]"
pip install numpy==1.26.0 # revert to numpy version is 1.26
```

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

```bash
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 the ```--eval.batch_size```
  flag.
</Tip>

### What to Expect

During evaluation, you will see a progress bar showing the running success rate:

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

### Video Recording

To enable video recording during evaluation, add the following flags to your command:

```bash
--env.video=true \
--env.video_length=15 \
--env.video_interval=15
```

For more details on video recording, see the [IsaacLab Recording Documentation](https://isaac-sim.github.io/IsaacLab/main/source/how-to/record_video.html).

<Tip>
When running headless with `--env.headless=true`, you must also enable cameras explicitly for camera enabled environments:

```bash
--env.headless=true --env.enable_cameras=true
```

</Tip>

### Output Directory

Evaluation videos are saved to the output directory with the following structure:

```
outputs/eval/<date>/<timestamp>_<env>_<policy>/videos/<task>_<env_id>/eval_episode_<n>.mp4
```

For example:

```
outputs/eval/2026-01-02/14-38-01_isaaclab_arena_smolvla/videos/gr1_microwave_0/eval_episode_0.mp4
```

## Training Policies

To learn more about training policies with LeRobot, please refer to training documentation:

- [SmolVLA](./smolvla)
- [Pi0.5](./pi05)
- [GR00T N1.5](./groot)

Sample IsaacLab Arena datasets are available on HuggingFace Hub for experimentation:

| 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    |

## Environment Configuration

### Full Configuration Options

```python
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 Environment Hub directly for advanced usage

Create a file called `test_env_load_arena.py` or [download from the EnvHub](https://huggingface.co/nvidia/isaaclab-arena-envs/blob/main/tests/test_env_load_arena.py):

```python
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 zero 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
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
```

## Troubleshooting

### CUDA out of memory

Reduce `batch_size` or use a GPU with more VRAM:

```bash
--eval.batch_size=1
```

### EULA not accepted

Set environment variables before running:

```bash
export ACCEPT_EULA=Y
export PRIVACY_CONSENT=Y
```

### Video recording not working

Enable cameras when running headless:

```bash
--env.video=true --env.enable_cameras=true --env.headless=true
```

### Policy output dimension mismatch

E.g. ensure `action_dim` matches your policy:

```bash
--env.action_dim=36
```

### libGLU.so.1 Errors during Isaac Sim initialization

Ensure you have the following dependencies installed, this is likely to happen on headless machines.

```bash
sudo apt update && sudo apt install -y libglu1-mesa libxt6
```

## See Also

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

## LightWheel LW-BenchHub

[LightWheel AI](https://www.lightwheel.ai) are bringing `Lightwheel-Libero-Tasks` and `Lightwheel-RoboCasa-Tasks` with 268 tasks to the LeRobot ecosystem.
LW-BenchHub collects and generates large-scale datasets via teleoperation that comply with the LeRobot specification, enabling out-of-the-box training and evaluation workflows.
With the unified interface provided by EnvHub, developers can quickly build end-to-end experimental pipelines.

### Install

Assuming you followed the [Installation](#installation) steps, you can install LW-BenchHub with:

```bash
conda install pinocchio -c conda-forge -y
git clone https://github.com/LightwheelAI/lw_benchhub
cd lw_benchhub
pip install -e .
```

For more detailed instructions, please refer to the [LW-BenchHub Documentation](https://docs.lightwheel.net/lw_benchhub/usage/Installation).

### Lightwheel Tasks Dataset

LW-BenchHub datasets are available on HuggingFace Hub:

| 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  |

For training policies, refer to the [Training Policies](#training-policies) section.

### Evaluating Policies

#### Pre-trained Policies

The following trained policies are available:

| 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/tree/main) |

#### Evaluate SmolVLA

```bash
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
```

### Environment Configuration

Evaluation can be quickly launched by modifying the `robot`, `task`, and `layout` settings in the configuration file.

#### Full Configuration Options

```yml
# =========================
# Basic Settings
# =========================
disable_fabric: false
device: cuda:0
sensitivity: 1.0
step_hz: 50
enable_cameras: true
execute_mode: eval
episode_length_s: 20.0 # Episode length in seconds, increase if episodes timeout during eval

# =========================
# Robot Settings
# =========================
robot: DoublePiper-Abs # Robot type, DoublePiper-Abs, X7S-Abs, G1-Controller or G1-Controller-DecoupledWBC
robot_scale: 1.0

# =========================
# Task & Scene Settings
# =========================
task: L90K1PutTheBlackBowlOnThePlate # Task name
scene_backend: robocasa
task_backend: robocasa
debug_assets: null
layout: libero-1-1 # Layout and style ID
sources:
  - objaverse
  - lightwheel
  - aigen_objs
object_projects: []
usd_simplify: false
seed: 42

# =========================
# Object Placement Retry Settings
# =========================
max_scene_retry: 4
max_object_placement_retry: 3

resample_objects_placement_on_reset: true
resample_robot_placement_on_reset: true

# =========================
# Replay Configuration Settings
# =========================
replay_cfgs:
  add_camera_to_observation: true
  render_resolution: [640, 480]
```

### See Also

- [LW-BenchHub GitHub](https://github.com/LightwheelAI/LW-BenchHub)
- [LW-BenchHub Documentation](https://docs.lightwheel.net/lw_benchhub/)