Authors: Raffaello Bonghi, Lior Ben Horin, Kartik S., Kalyan Vadrevu, Steven Palma, Jade Choghari (Hugging Face)

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Robots must perceive unstructured environments, reason and plan under uncertainty, and execute actions safely in real time on physical systems. To do this, they need to develop an understanding of the physical world through three forms of computational intelligence: training, simulation, and on‑robot inference. Each stage relies on distinct hardware and software, and each plays a critical role in the end‑to‑end robotics development pipeline.

NVIDIA and Hugging Face are integrating NVIDIA’s open Isaac and GR00T technologies—including robot simulation and learning frameworks, models, and hardware systems—across these three computers into the LeRobot library. This will accelerate open‑source physical AI development by bringing NVIDIA’s 2 million robotics developers and Hugging Face’s more than 13 million AI builders together.

Developers get access to open‑source pre‑trained Isaac GR00T N vision‑language‑action (VLA) models, physical‑AI datasets, evaluation frameworks like NVIDIA Isaac Lab‑Arena, and hardware platforms such as the Reachy 2 humanoid running on NVIDIA Jetson Thor.

In this blog we show how to evaluate VLA policies using Isaac Lab‑Arena in LeRobot EnvHub, making robot environments easier to author, share, and reuse across the community.

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NVIDIA Isaac Lab‑Arena and LeRobot Integration

Isaac Lab‑Arena is an open‑source framework for efficient and scalable robotic policy evaluation in simulation, with the evaluation and task layers designed in close collaboration with Lightwheel.

Hugging Face’s LeRobot EnvHub feature enables developers to share simulation environments and easily load them for training, evaluation, or teleoperation, directly from the LeRobot framework.

Isaac Lab‑Arena is now integrated into Hugging Face’s LeRobot Environment Hub, making it easier for developers to build, experiment with, and collaborate on robotics simulation. Developers can now prototype complex and diverse simulation environments using Isaac Lab‑Arena, register these environments on LeRobot EnvHub, and seamlessly use them to train and evaluate robot policies (e.g., GR00T N, Pi, SmolVLA, among others) within the LeRobot ecosystem.

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How to Evaluate a VLA on Isaac Lab‑Arena Environments Available on LeRobot

Prerequisites

• Hardware requirements for Isaac Lab‑Arena are shared with Isaac Sim (see the Isaac Sim quick‑start guide).

• NVIDIA driver compatible with IsaacSim 5.1.0.

• Linux (Ubuntu 22.04 or 24.04).

Setup

# 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 Isaac Lab‑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 (will be fixed in next release)

Evaluate SmolVLA

# Install SmolVLA (and ensure numpy version)

pip install -e ".[smolvla]"

pip install numpy==1.26.0   # revert to numpy version 1.26

Run the LeRobot evaluation on Isaac Lab‑Arena:

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

For a deeper dive (including evaluation of PI0.5) see the LeRobot documentation.

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How to Create and Register New Isaac Lab‑Arena Environments on LeRobot EnvHub

While the previous section demonstrated a sample workflow using existing Isaac Lab‑Arena environments on EnvHub, developers are encouraged to share custom environments with the community.

1. Create your own environments and task suites on Isaac Lab‑Arena, and easily swap embodiments, objects, and scenarios (see the Isaac Lab‑Arena documentation).

2. Register your Isaac Lab‑Arena‑based environments on LeRobot EnvHub (see the LeRobot EnvHub guide).

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Lightwheel Robocasa and LIBERO Task Suites on Isaac Lab‑Arena

Lightwheel has adopted the Isaac Lab‑Arena framework to create and open‑source 250+ tasks through the Lightwheel‑RoboCasa‑Tasks and Lightwheel‑LIBERO‑Tasks suites. LeRobot developers can now seamlessly use these tasks thanks to this integration.

Once a policy has been evaluated in simulation, it can be deployed on robot systems such as Reachy 2, powered by NVIDIA Jetson Thor ⚡.

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Get Started

Additional resources to explore Isaac Lab‑Arena and LeRobot:

• Collect and prepare data: Capture real‑world data using the SO‑101 robot within a simulated Isaac Lab environment, and post‑train policies using open‑source datasets on Hugging Face (e.g., the Lightwheel Tasks Dataset).

• Work with open VLA models: Download open GR00T N models and review the LeRobot GR00T N 1.5 policy documentation. Watch the technical livestream with the Hugging Face team for a deep dive into GR00T N 1.5 and LeRobot integration.

• Train and evaluate at scale: Isaac Lab‑Arena pre‑alpha is open source. Build environments, benchmark policies, and contribute reusable environments through the LeRobot Environment Hub. Get started with the GitHub repository, the technical blog, and the official documentation.

• Get started easily with NVIDIA Brev: Use NVIDIA Brev to quickly provision GPU instances for NVIDIA Isaac Lab workloads, and watch the Brev technical livestream for guidance on running Isaac Lab efficiently.