How to Set Up NVIDIA Isaac Lab: A Practical Guide

TL;DR

Install Isaac Lab via Omniverse Launcher or Docker for GPU-accelerated RL training.
Design environments using USD scenes and Hydra for domain randomization.
Train PPO/RSL-RL policies with isaaclab train and scale to 1000+ parallel robots.
Export policies to ROS 2 or Omniverse for real-world deployment.
Benchmark throughput with isaaclab benchmark and optimize GPU utilization.

1. Installation and Isaac Sim Dependency

Isaac Lab is built on top of Isaac Sim, so you’ll need to install both. Follow these steps for a Linux (Ubuntu 22.04 LTS) setup, the officially supported platform.

Option 1: Omniverse Launcher (Recommended)

# Download and install Omniverse Launcher
wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/cuda-ubuntu2204.pin
sudo mv cuda-ubuntu2204.pin /etc/apt/preferences.d/cuda-repository-pin-600
sudo apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/3bf863cc.pub
sudo add-apt-repository "deb https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/ /"
sudo apt-get update
sudo apt-get -y install cuda

# Install Omniverse Launcher
wget https://developer.download.nvidia.com/compute/cuda/repos/omniverse2026.1/ubuntu2204/omniverse-launcher_2026.1.0-1_amd64.deb
sudo dpkg -i omniverse-launcher_2026.1.0-1_amd64.deb
sudo apt install -f

# Launch Omniverse Launcher and install Isaac Sim 2026.1 + Isaac Lab extension
omniverse-launcher

In the Launcher, go to Isaac Sim 2026.1 → Extensions → Enable Isaac Lab.
Launch Isaac Sim and verify the Isaac Lab tab appears.

Option 2: Docker (For Cloud/Reproducibility)

# Pull the latest Isaac Lab container
docker pull nvcr.io/nvidia/isaac-sim:2026.1.0-isaaclab

# Run with GPU access (replace `your_username` with your actual username)
docker run --gpus all -it --rm \
  -v /tmp/.X11-unix:/tmp/.X11-unix \
  -e DISPLAY=$DISPLAY \
  -e NVIDIA_DRIVER_CAPABILITIES=compute,utility \
  -e NVIDIA_VISIBLE_DEVICES=all \
  --name isaaclab \
  nvcr.io/nvidia/isaac-sim:2026.1.0-isaaclab

# Inside the container, verify installation
isaaclab --version

Expected Output:

Isaac Lab v1.2 (Isaac Sim 2026.1.0)

Option 3: Source Installation (Advanced)

git clone https://github.com/isaac-sim/IsaacLab.git
cd IsaacLab
./isaaclab.sh --install

Requires Python 3.10+, PyTorch 2.1.0, and CUDA 12.4.
Follow the source install guide for dependencies.

2. Environment and Task Design

Isaac Lab uses USD (Universal Scene Description) for scene composition. Design your robot environment with Hydra or USD Composer.

Step 1: Create a Basic USD Scene

# Navigate to Isaac Lab examples
cd ~/isaaclab/examples/rl

# Launch a pre-built environment (e.g., Franka Cube)
isaaclab launch --scene franka_cube.usd

Expected Output:

A <a href="/services/digital-twin-consulting">simulation</a> window opens with a Franka robot and a cube.

Step 2: Customize Your Environment

Edit the USD scene file (franka_cube.usd) or create a new one:

# Example: Modify the scene using USD Python API
from pxr import Usd, UsdGeom, Sdf

stage = Usd.Stage.CreateNew("custom_scene.usd")
robot = Sdf.CreatePrim("/World/Franka", "Xform")
robot.AddAttribute("type", "franka_emika_panda")
stage.GetRootLayer().Save()

Use USD Composer (included in Omniverse) for visual editing.

Step 3: Define Observations and Actions

Configure observations (camera, proprioception) and actions (joint torques) in a Hydra config file (config.yaml):

# ~/isaaclab/configs/franka_cube/config.yaml
env:
  name: "FrankaCubeEnv"
  num_envs: 1024  # Parallel environments
  obs:
    type: "camera"
    resolution: [256, 256]
    num_cameras: 2
  act:
    type: "joint_torques"
    dof: 7

Save the file in ~/isaaclab/configs/your_env/.

3. Massively Parallel RL (PPO, RSL-RL)

Isaac Lab supports Proximal Policy Optimization (PPO) and RSL-RL (NVIDIA’s RL library).

Step 1: Train a PPO Policy

# Train PPO on the Franka Cube task
isaaclab train \
  --config configs/franka_cube/config.yaml \
  --algorithm ppo \
  --num_steps 1e6 \
  --save_freq 10000 \
  --output_dir ~/isaaclab_runs/franka_ppo

Key Arguments:

--algorithm: ppo or rsl_rl (NVIDIA’s optimized RL library).
--num_steps: Total training steps (e.g., 1e6).
--num_envs: Parallel environments (default: 1024).

Step 2: Monitor Training

# Launch TensorBoard to visualize metrics
isaaclab tensorboard --logdir ~/isaaclab_runs/franka_ppo

Expected Output:

Open http://localhost:6006 to see training curves (reward, loss, etc.).

Step 3: Scale to Multi-GPU

# Train on 4 GPUs (requires NCCL and CUDA 12.4)
isaaclab train \
  --config configs/franka_cube/config.yaml \
  --algorithm ppo \
  --num_gpus 4 \
  --num_envs_per_gpu 256 \
  --num_steps 1e7

Gotcha: Ensure NCCL_DEBUG=INFO is set if training hangs:

export NCCL_DEBUG=INFO

4. Imitation Learning Workflows

Isaac Lab supports Behavior Cloning (BC) and DDPG for imitation learning.

Step 1: Prepare Dataset

# Record expert demonstrations (e.g., from a real robot or kinematic solver)
isaaclab record \
  --scene franka_cube.usd \
  --output_dir ~/expert_demos \
  --num_episodes 100

Output: Saves trajectories in ~/expert_demos/trajectories.hdf5.

Step 2: Train a BC Policy

isaaclab train \
  --config configs/franka_cube/config.yaml \
  --algorithm bc \
  --dataset ~/expert_demos/trajectories.hdf5 \
  --batch_size 256

Key Arguments:

--algorithm: bc (Behavior Cloning) or ddpg (Deep Deterministic Policy Gradient).

5. Domain Randomization and Curricula

Domain randomization improves generalization by varying simulation parameters.

Step 1: Enable Randomization

Edit config.yaml:

env:
  randomization:
    enable: true
    ranges:
      friction: [0.5, 1.5]
      mass: [0.8, 1.2]
      lighting: [0.7, 1.3]

Step 2: Curriculum Learning

Use a curriculum script (curriculum.py) to gradually increase task difficulty:

def get_curriculum(env, step):
    if step < 1e5:
        return {"target_pos": [0.1, 0, 0.1]}  # Easy
    elif step < 5e5:
        return {"target_pos": [0.2, 0, 0.2]}  # Medium
    else:
        return {"target_pos": [0.3, 0, 0.3]}  # Hard

Pass the curriculum to training:

isaaclab train \
  --config configs/franka_cube/config.yaml \
  --algorithm ppo \
  --curriculum curriculum.py

6. Exporting Policies for Deployment

Export trained policies to ROS 2 or Omniverse for real-world deployment.

Step 1: Export to ROS 2

# Export the trained policy to a ROS 2 package
isaaclab export \
  --input_dir ~/isaaclab_runs/franka_ppo \
  --output_dir ~/franka_ros2_policy \
  --format ros2 \
  --robot_model franka_emika_panda

Output: Generates a ROS 2 node with the policy.

Step 2: Deploy to a Real Robot

# Run the policy on a real Franka robot (requires Isaac ROS)
ros2 launch franka_ros2_policy franka_policy.launch.py

Step 3: Export to Omniverse

isaaclab export \
  --input_dir ~/isaaclab_runs/franka_ppo \
  --output_dir ~/omniverse_policy \
  --format omniverse

Output: A USD-based policy that can be loaded in Omniverse Kit.

7. Benchmarking Training Throughput

Measure GPU utilization and training speed.

Step 1: Benchmark Parallel Environments

isaaclab benchmark \
  --config configs/franka_cube/config.yaml \
  --num_envs 1024 \
  --num_gpus 1

Expected Output:

Throughput: 1200 env steps/sec
GPU Utilization: 92%

Step 2: Optimize Performance

Reduce observation resolution if GPU memory is limited.
Use mixed precision (fp16):
```
algorithm:
  ppo:
    use_fp16: true
```

What’s Next?

Try a Pre-Trained Policy: Load a pre-trained policy from NVIDIA’s Isaac Lab Models.
Experiment with Diffusion Policies: Use the new diffusion algorithm in Isaac Lab v1.2 for dexterous manipulation.
Deploy to Cloud: Scale training on AWS/GCP using NVIDIA NGC containers.

For AI tools consulting or Physical AI infrastructure, visit Hyperion Consulting.

How to Set Up NVIDIA Isaac Lab: A Practical Guide

1. Installation and Isaac Sim Dependency

Option 1: Omniverse Launcher (Recommended)

Option 2: Docker (For Cloud/Reproducibility)

Option 3: Source Installation (Advanced)

2. Environment and Task Design

Step 1: Create a Basic USD Scene

Step 2: Customize Your Environment

Step 3: Define Observations and Actions

3. Massively Parallel RL (PPO, RSL-RL)

Step 1: Train a PPO Policy

Step 2: Monitor Training

Step 3: Scale to Multi-GPU

4. Imitation Learning Workflows

Step 1: Prepare Dataset

Step 2: Train a BC Policy

5. Domain Randomization and Curricula

Step 1: Enable Randomization

Step 2: Curriculum Learning

6. Exporting Policies for Deployment

Step 1: Export to ROS 2

Step 2: Deploy to a Real Robot

Step 3: Export to Omniverse

7. Benchmarking Training Throughput

Step 1: Benchmark Parallel Environments

Step 2: Optimize Performance

What’s Next?

Sources

The 30% Report

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