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Module 1: The Robotic Nervous System (ROS 2)

Master the middleware that connects robotic intelligence to physical hardware

Module Overview​

This module teaches you ROS 2 (Robot Operating System 2), the middleware framework that enables complex robotic systems. You'll learn how robots communicate, coordinate actions, and integrate sensors through a distributed architecture designed for real-time physical systems.

By the end of this module, you will:

  • Understand Physical AI and how it differs from digital AI
  • Build and run ROS 2 nodes using Python
  • Implement publish-subscribe, service, and action communication patterns
  • Create URDF models describing humanoid robot structure
  • Integrate sensors and actuators through ROS 2

Prerequisites​

  • Python Basics: Variables, functions, loops, classes
  • Command Line: Basic terminal usage (cd, ls, mkdir)
  • Development Environment: Ubuntu 22.04 or WSL2 with ROS 2 Humble/Iron installed

No robotics knowledge required - we start from foundational concepts.

Module Structure​

Chapter 1: Introduction to Physical AI​

Type: Theory-Only (Foundation)
Lessons: 8
Duration: 8-10 hours

You will learn:

  • What Physical AI is and why it matters
  • Real-world constraints that make robotics challenging
  • Current humanoid robot landscape (Atlas, Optimus, Figure 01)
  • Sensor systems: LIDAR, cameras, IMUs, force/torque sensors
  • Why sensor fusion is critical for robust perception

→ Start Chapter 1

Chapter 2: ROS 2 Architecture and Core Concepts​

Type: Theory-to-Practice
Lessons: 8 (5 theory + 3 code)
Duration: 12-16 hours

You will learn:

  • ROS 2 as middleware (not an operating system)
  • DDS distributed architecture and why it replaced ROS 1
  • Nodes, topics, services, and actions communication patterns
  • Creating your first minimal ROS 2 node
  • QoS (Quality of Service) policies for reliable communication
  • Launch files for multi-node orchestration

→ Start Chapter 2

Chapter 3: Python-ROS Integration with rclpy​

Type: Practice-Heavy
Lessons: 8
Duration: 14-18 hours

You will learn:

  • Publishers and subscribers for streaming data
  • Service servers and async clients for request-response
  • Parameters for runtime configuration
  • Callback groups for concurrent operations
  • Timers for multi-rate control loops
  • Executors for system composition
  • Capstone: 3-node sensor-filter-controller system

→ Start Chapter 3

Chapter 4: Robot Description with URDF and Xacro​

Type: Theory-to-Practice
Lessons: 8 (3 theory + 5 code)
Duration: 16-20 hours

You will learn:

  • URDF as the blueprint for robot structure
  • Links, joints, and kinematic chains
  • Visual vs collision geometry trade-offs
  • Xacro for parameterized, reusable robot descriptions
  • Bipedal humanoid design considerations
  • Sensor integration (cameras, LIDAR, IMU) in URDF
  • Validation and visualization with RViz

→ Start Chapter 4

Module Learning Path​

Chapter 1: Physical AI Foundations
    ↓ (Understand WHAT robots need and WHY)
Chapter 2: ROS 2 Architecture
    ↓ (Learn HOW robots communicate)
Chapter 3: Python/rclpy Integration
    ↓ (Build complete robot control systems)
Chapter 4: URDF Robot Modeling
    ↓ (Define robot physical structure)
───────────────────────────────────
Module 1 Complete! → Module 2: Simulation

Estimated Time Commitment​

ComponentTime
Reading & Theory20-25 hours
Hands-On Coding20-30 hours
Challenges & Projects10-15 hours
Total Module 150-70 hours

Recommended Pace: 10-15 hours per week = 4-6 weeks to complete

What You'll Build​

Chapter 2 Projects​

  • Minimal ROS 2 "Hello World" node
  • QoS-configured sensor publisher
  • Multi-node system with launch file

Chapter 3 Projects​

  • Temperature sensor publisher-subscriber pair
  • Validation service (request-response)
  • Parameter-driven configurable node
  • Multi-rate sensor fusion controller
  • Capstone: 3-node perception pipeline

Chapter 4 Projects​

  • Parameterized humanoid leg URDF
  • Bilateral arm macro with Xacro
  • Complete humanoid with integrated sensors
  • Capstone: Validated full humanoid model in RViz

Success Criteria​

You have successfully completed Module 1 when you can:

  • Explain Physical AI and sensor fusion principles
  • Create and run ROS 2 nodes using Python
  • Implement all communication patterns (topics, services, actions)
  • Design multi-node systems with launch files
  • Build URDF models for humanoid robots
  • Integrate sensors and validate robot descriptions

Tools & Resources​

Required Software​

  • ROS 2: Humble or Iron (Ubuntu 22.04 / WSL2)
  • Python: 3.10+
  • Colcon: ROS 2 build tool
  • RViz: Visualization tool (included with ROS 2)

Installation Guides​

Reference Documentation​

Ready to Begin?​

Start with Chapter 1: Introduction to Physical AI

Master the foundational concepts before diving into ROS 2 implementation.

Start Chapter 1 →