Isaac Lab – Running Demo Scripts and Understanding Core Keywords

Isaac Lab is a powerful reinforcement learning framework built on top of NVIDIA Isaac Sim, designed to streamline the creation, testing, and evaluation of robotic learning environments.

In this post, we’ll walk through how to run one of the key demo scripts provided by Isaac Lab, and explain the core components of its simulation pipeline.

Specifically, we'll explore how to run the Showroom Demos locally, and break down functions like environment setup, robot spawning, scene design, and randomized motion initialization.

What is the Isaac Lab Showroom?

Isaac Lab offers a collection of demo scripts known as Showroom Demos, which showcase different robot types and behaviors in simulation.

Official docs:

🔗 Isaac Lab Showroom Demos

Available Demo Scripts

Some of the built-in demo scripts you can run include:

• quadrupeds.py – Simulates quadrupedal robots

• amrs.py – Simulates autonomous mobile robots (AMRs)

• hands.py – Demonstrates robot hand simulations

1. Installing Isaac Lab and Running a Demo

To get started:

1. Clone the Isaac Lab GitHub repository
2. Navigate to the directory, e.g., /isaac/isaacLab
3. Run a demo script using:

2. Script Overview: quadrupeds.py

The script uses robot configurations from omni.isaac.lab_assets, which define the physical properties and control setup of various quadrupeds.

This modular setup allows you to quickly test different robot types with minimal changes to the code.

3. The define_origins() Function

This function defines the initial positions of all environments (i.e., individual robot instances).

It returns a tensor of shape (num_envs x 3), representing the x, y, z coordinates.

• num_cols: Number of environments per row
• num_rows: Computed based on total environments

This setup allows dozens or even hundreds of robots to be placed in a grid on a single simulation floor.

4. The design_scene() Function

This function constructs the simulation scene, including:

• A ground plane
• Light sources
• Environment-wide lighting intensity

While these elements can be added manually in Isaac Sim’s UI, automating them in code ensures reproducibility across training runs.

5. Initializing Origins and Running the Simulator

• origin은 학습에 사용될 로봇들의 위치를 일괄 지정합니다.

• The run_simulator() function starts the simulation once all components are defined

You can also configure the observation perspective using sim.set_camera_view() to define the camera angle.

6. Randomizing Robot Actions at Start

Each robot is automatically spawned into the simulation, and joint positions can be initialized with random noise:

This helps avoid overfitting and encourages learning generalizable behaviors.

7. Changing the Randomization Range: 0.1 → 5.1

The standard deviation of the noise has a significant effect:

• 0.1: Minor random deviations → stable behavior

• 5.1: Large variability → more chaotic but diverse movement

Choosing an appropriate scale depends on the complexity of the learning task and your training objectives.

Conclusion

In this post, we reviewed the quadrupeds.py demo script from Isaac Lab and broke down its major components:

• Environment layout with define_origins()
• Scene creation with design_scene()
• Randomized behavior for training robustness
• Joint initialization and simulation control

Isaac Lab is a powerful tool for:

• Multi-agent simulation
• Reinforcement learning (RL) training environments
• Joint control and locomotion testing
• Camera and rendering pipeline integration

If you're planning to develop or test RL policies using Isaac Sim, Isaac Lab gives you a clean and extensible foundation.