# 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.

[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**: Door opening, pick-and-place, button pressing, and more
- ⚡ **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 N1x, PI0, SmolVLA, ACT, and Diffusion policies
- 🔄 **EnvHub integration**: Load environments from HuggingFace EnvHub 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 numpy to version 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 numpy to version 1.26
```

PI0.5 requires disabling torch compile for evaluation:

```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
```

  To change the number of parallel environments, use the ```--eval.batch_size```
  flag.

### 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/__/videos/_/eval_episode_.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 the 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 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
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
```

## Creating New Environments

First create a new IsaacLab Arena environment by following the [IsaacLab Arena Documentation](https://isaac-sim.github.io/IsaacLab-Arena/release/0.1.1/index.html).

Clone our EnvHub repo:

```bash
git clone https://huggingface.co/nvidia/isaaclab-arena-envs
```

Modify the `example_envs.yaml` file based on your new environment.
[Upload](./envhub#step-3-upload-to-the-hub) your modified repo to HuggingFace EnvHub.

  Your IsaacLab Arena environment code must be locally available during
  evaluation. Users can clone your environment repository separately, or you can
  bundle the environment code and assets directly in your EnvHub repo.

Then, when evaluating, use your new environment:

```bash
lerobot-eval \
    --env.hub_path=/isaaclab-arena-envs \
    --env.environment= \
    ...other flags...
```

We look forward to your contributions!

## 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

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](https://www.lightwheel.ai) is 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
pip install numpy==1.26.0 # revert numpy to version 1.26

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

git clone https://github.com/LightwheelAI/lw_benchhub
git lfs pull # Ensure LFS files (e.g., .usd assets) are downloaded

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/)