Part 3: The AI-Robot Brain
NVIDIA Isaac Platform
You have ROS 2 for communication and Gazebo for simulation. Now add intelligence. NVIDIA Isaac brings GPU-accelerated AI to robotics: generate unlimited training data, deploy visual SLAM for localization, navigate autonomously with Nav2, and train policies with reinforcement learning.
This is where robots stop following scripts and start perceiving, understanding, and learning. Isaac Sim creates photorealistic synthetic data with automatic ground truth labels—no manual annotation. Isaac ROS deploys optimized perception pipelines to real hardware. And Isaac Lab trains robot policies in simulation that transfer to physical robots.
In this part, you'll deploy real perception systems and train your first robot policies. The skills you build here power the autonomous humanoid in Part 4.
Chapters
Chapter 6: Isaac Sim Overview
Your introduction to NVIDIA's simulation platform. You'll generate synthetic datasets with domain randomization (varying lighting, textures, and object positions so models generalize to the real world), explore Isaac Lab for robot training environments, and understand how synthetic data pipelines work. By the end, you'll have a dataset ready for training perception models.
Chapter 7: Isaac ROS Integration
Deploy perception to real robots. You'll implement VSLAM (Visual Simultaneous Localization and Mapping) for robot positioning, integrate Nav2 for autonomous navigation, and run everything in optimized Docker containers. Your robot will navigate environments it has never seen before, building maps as it goes.
Chapter 8: Reinforcement Learning (optional)
Train policies instead of programming them. You'll use Isaac Lab to train locomotion and manipulation skills through trial and error, understand sim-to-real transfer techniques, and see how modern robots learn complex behaviors. This chapter is optional but sets up the advanced work in Part 4.
Learning Outcomes
By completing Part 3, you will:
- Generate synthetic training data with domain randomization
- Deploy VSLAM for robot localization
- Implement autonomous navigation with Nav2
- Train robot policies using reinforcement learning
- Transfer learned behaviors from simulation to reality
Hardware
| Tier | What You Need | What You Get |
|---|---|---|
| 1 (Cloud) | Any laptop | NVIDIA Omniverse Cloud (free tier: 2 hrs/month) |
| 2 (Local) | RTX 3060+ GPU | Full local Isaac Sim + faster training |
| 3 (Edge) | Jetson Orin | Deploy to edge devices |
Capstone: AI-Powered Navigation
Build a system where your robot uses VSLAM to localize, Nav2 to plan paths, and Isaac ROS to process camera input—all running on real hardware or high-fidelity simulation. The robot navigates to goals in environments it has never seen, avoiding obstacles along the way.
Prerequisites
Parts 1-2 complete. You'll need ROS 2 nodes and Gazebo simulation experience.