NVIDIA IsaacLab Arena

IsaacLab Arena provides GPU-accelerated, high-fidelity humanoid manipulation environments for training and evaluating vision-language-action models at scale. IsaacLab Arena - GR1 Microwave Environment

IsaacLab Arena - GR1 Microwave Environment

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
📚 IsaacLab Documentation

Installation

Prerequisites

Hardware requirements (see Isaac Sim Requirements):

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

Setup Instructions

# 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
smolvla-arena-gr1-microwave	SmolVLA	GR1 Microwave	HuggingFace

Evaluating Policies

Evaluate SmolVLA

First, install SmolVLA dependencies:

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

Run evaluation:

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:

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

PI0.5 requires disabling torch compile for evaluation:

Run evaluation:

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 --eval.batch_size.

Expected Output

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

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	GR1 microwave manipulation	~4K
Arena-G1-Loco-Manipulation-Task	G1 loco-manipulation	~4K

Training example:

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:

Environment Configuration

Full Configuration Options

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:

# 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:

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:

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

When running headless, explicitly enable cameras:

--env.headless=true --env.enable_cameras=true

Output Directory

Videos are saved to:

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

Example:

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

See IsaacLab Recording Documentation for details.

Creating New Environments

Create IsaacLab Arena environment: Follow IsaacLab Arena Documentation

Clone EnvHub repo:

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

Modify example_envs.yaml based on your environment
Upload to EnvHub: See EnvHub guide

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

Evaluate with your environment:

lerobot-eval \
    --env.hub_path=<your-username>/isaaclab-arena-envs \
    --env.environment=<your-new-environment> \
    ...other flags...

Lightwheel LW-BenchHub

Lightwheel provides 268 tasks across LIBERO and RoboCasa with large-scale datasets:

Installation

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 for details.

Datasets

Dataset	Description	Tasks	Frames
Lightwheel-Tasks-X7S	X7S LIBERO and RoboCasa	117	~10.3M
Lightwheel-Tasks-Double-Piper	Double-Piper LIBERO	130	~6.0M
Lightwheel-Tasks-G1-Controller	G1-Controller LIBERO	62	~2.7M
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

Evaluate SmolVLA on LW-BenchHub

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:

--eval.batch_size=1

EULA not accepted

Set environment variables:

export ACCEPT_EULA=Y
export PRIVACY_CONSENT=Y

Video recording not working

Enable cameras when running headless:

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

Policy output dimension mismatch

Ensure action_dim matches your policy:

--env.action_dim=36

libGLU.so.1 Errors

Install missing dependencies:

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

Get Started

Core Concepts

Tutorials

Datasets

Simulation

Inference

Advanced

Isaaclab arena

NVIDIA IsaacLab Arena

Installation

Prerequisites

Setup Instructions

Pre-trained Policies

Evaluating Policies

Evaluate SmolVLA

Evaluate PI0.5

Expected Output

Training Policies

Environment Configuration

Full Configuration Options

Using EnvHub Directly

Video Recording

Output Directory

Creating New Environments

Lightwheel LW-BenchHub

Installation

Datasets

Pre-trained Policies

Evaluate SmolVLA on LW-BenchHub

Troubleshooting

CUDA out of memory

EULA not accepted

Video recording not working

Policy output dimension mismatch

libGLU.so.1 Errors

See Also

Build docs developers (and LLMs) love

Get Started

Core Concepts

Tutorials

Datasets

Simulation

Inference

Advanced

​NVIDIA IsaacLab Arena

​Installation

​Prerequisites

​Setup Instructions

​Pre-trained Policies

​Evaluating Policies

​Evaluate SmolVLA

​Evaluate PI0.5

​Expected Output

​Training Policies

​Environment Configuration

​Full Configuration Options

​Using EnvHub Directly

​Video Recording

​Output Directory

​Creating New Environments

​Lightwheel LW-BenchHub

​Installation

​Datasets

​Pre-trained Policies

​Evaluate SmolVLA on LW-BenchHub

​Troubleshooting

​CUDA out of memory

​EULA not accepted

​Video recording not working

​Policy output dimension mismatch

​libGLU.so.1 Errors

​See Also

Build docs developers (and LLMs) love

NVIDIA IsaacLab Arena

Installation

Prerequisites

Setup Instructions

Pre-trained Policies

Evaluating Policies

Evaluate SmolVLA

Evaluate PI0.5

Expected Output

Training Policies

Environment Configuration

Full Configuration Options

Using EnvHub Directly

Video Recording

Output Directory

Creating New Environments

Lightwheel LW-BenchHub

Installation

Datasets

Pre-trained Policies

Evaluate SmolVLA on LW-BenchHub

Troubleshooting

CUDA out of memory

EULA not accepted

Video recording not working

Policy output dimension mismatch

libGLU.so.1 Errors

See Also