Who is this for?
This is an advanced topic for robotics developers, simulation engineers, and AI researchers who want to run high-fidelity robotic simulations and reinforcement learning (RL) pipelines using NVIDIA Isaac Sim and Isaac Lab on Arm-based NVIDIA DGX Spark system powered by the Grace–Blackwell (GB10) architecture.
What will you learn?
Upon completion of this Learning Path, you will be able to:
- Describe the roles of Isaac Sim and Isaac Lab within a robotics simulation and RL pipeline
- Build and configure Isaac Sim and Isaac Lab on an Arm-based DGX Spark system
- Launch and control a robot simulation in Isaac Sim using Python
- rain and evaluate a reinforcement learning policy for the Unitree H1 humanoid robot using Isaac Lab and RSL-RL
Prerequisites
Before starting, you will need the following:
- A NVIDIA DGX Spark system with at least 50 GB of free disk space
- Familiarity with Linux command-line tools
- Experience with Python scripting and virtual environments
- Basic understanding of reinforcement learning concepts (rewards, policies, episodes)