Simulating a Quadruped – Setting the Foundation for Learning and Control
With reinforcement learning (RL) and other AI techniques becoming more prevalent in robotics, I've been wanting to explore what can be done with robotics today. What better way to learn than to dive deep into a project. So, I’ve decided to build a quadruped robot as a first attempt into RL. Quadrupeds are some of the more widely used robots that utilize RL, so it appears to be a good place to start. Time will tell...
Before diving into hardware, I need to answer some big questions: How do I size the motors correctly? How do I test different control strategies efficiently? Does the robot even have a chance at walking? Instead of learning these lessons the hard way (i.e., wasting money on parts that don’t work), I’m starting with simulation.
This first phase is all about setting up the robot in the simulation environment, testing basic movement, and laying the groundwork for more advanced control strategies. I’ll be using Isaac Sim and Isaac Lab for physics-based simulation and ROS 2 for control integration. The goal is simple: get a quadruped standing and moving in a virtual world before committing to hardware.
Here are my first steps.
Step 1: Simulating the Basic Quadruped Model
In order to get rough sizing of the robot and motors, I'll first do a simplified quadruped from basic shapes. This way the robot can be reconfigured easily for sizing and different leg configurations. Here’s what I considered while iterating:
Frame Size & Weight Distribution – The center of mass plays a big role in stability. Too high, and the robot tips over easily; too low, and it might struggle to lift its legs properly.
Motor Selection – Motors can be costly. There needs to be at least 8 for a basic quadruped, 12 if you want shoulder rotation. I want to validate the motors with a basic design before committing to them.
Joints & Actuation – The number of degrees of freedom (DOF) affects how naturally the robot moves. Four legs with two or three joints each is a good starting point.
Step 2: Setting Up the Simulation Environment
Once the quadruped is modeled, it’s time to put it in a virtual world where I can test movement and physics interactions. Isaac Sim is my tool of choice because:
It runs GPU-accelerated physics simulations, making it great for real-time testing.
It integrates with ROS 2, so I can use the same control software in both simulation and real-world deployment.
It supports robot learning workflows, making it ideal for reinforcement learning down the line.
Key Components in the Simulation Setup:
URDF Model – Defines the robot’s physical structure.
Basic Controllers – Simple position controllers to make sure the legs move as expected.
Simulated Sensors – IMU (inertial measurement unit) and encoders to provide feedback.
My initial setup of Isaac Sim, Isaac Lab, and ROS 2 took quite a bit of time. And, I still have some refining and learning to do in this new environment (for me).
Closing
Simulation is supposedly a powerful tool for debugging design choices before they become real-world problems. However, I would like to try it out for myself. Visit my page again for updates as I go through the process. Anything that I found particularly hard, I will share so others can go through less pain than I.
