Train Your First Robot in Isaac Lab#

Welcome to the module, Train Your First Robot in Isaac Lab!

Reinforcement learning (RL) is an exciting, core topic in physical AI. It defines a dynamic, adaptable strategy for training robot brains, and underlies some of the most fascinating developments in robotics from the past decade.

While RL is a deep and technically rich topic, our goal with this module is to get you up and running quickly, and to walk you through a complete example workflow in simulation to start your learning journey.

In this module, we will:

  • Learn about Physical AI, and how it’s changing the ways robots learn and perform tasks.

  • Describe the core principles of reinforcement learning (RL) and their relevance to robotics using Isaac Lab and Isaac Sim.

  • Identify and configure the essential components of an RL task within Isaac Lab.

  • Apply an Isaac Lab workflow to train, evaluate, and refine a robot control policy in simulation, using the cartpole.

  • Analyze training results of learned policies.

Physical AI and Robots of the Future#

As robotics grow in capabilities, the ways we train them must also evolve.

Reinforcement learning helps us make robots adaptable to the real world by training against a wide variety of scenarios in simulation. We can also test robot behavior rigorously in simulation, without crashing expensive prototypes, before moving to physical testing.

You may have heard of both Isaac Sim, and now Isaac Lab, and wondered how they relate. Today you’ll learn to use NVIDIA’s GPU-accelerated robotics tools, including Isaac Sim for simulation, and the Isaac Lab framework for reinforcement learning, to teach a robot brain.

In later modules, we’ll cover more details for more sophisticated robots and eventually the sim-to-real transfer, which is the process of taking what you built here, and refining it to run on physical robots.

A fleet of cartpoles balancing in simulation. This is an example of the policy we will train in this lesson.

Let’s get started!