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Weekly Roadmap

Weekly Roadmap

This page outlines the weekly roadmap for the Physical AI & Humanoid Robotics Textbook, providing a structured learning path through all modules and key concepts.

Figure 1: Overview of the 7-week learning path through the Physical AI & Humanoid Robotics curriculum.

Week 1: ROS 2 Basics​

Learning Objectives​

  • Understand the fundamental concepts of ROS 2 and its architecture
  • Install and configure ROS 2 Humble Hawksbill
  • Learn about nodes, packages, and the ROS 2 ecosystem
  • Set up your development environment for robotics programming

Topics Covered​

  • Introduction to ROS 2 concepts and architecture
  • Installing ROS 2 on your system
  • Understanding the difference between ROS 1 and ROS 2
  • Setting up your first ROS 2 workspace
  • Basic ROS 2 commands and tools

Activities​

  • Install ROS 2 Humble Hawksbill on your development machine
  • Create your first ROS 2 workspace and package
  • Run basic ROS 2 commands (ros2 run, ros2 topic, ros2 service)
  • Explore the ROS 2 documentation and tutorials

Figure 2: ROS 2 architecture showing nodes, topics, services, and the DDS communication layer.

Resources​

Estimated Time​

8-10 hours of study and hands-on practice

Week 2: ROS 2 Nodes, Topics, URDF​

Learning Objectives​

  • Create and run ROS 2 nodes in Python and C++
  • Implement communication between nodes using topics
  • Define robot structure using URDF (Unified Robot Description Format)
  • Understand message passing and service architecture

Topics Covered​

  • Creating ROS 2 nodes in Python and C++
  • Publisher and subscriber patterns
  • Topics vs services vs actions
  • URDF for robot description
  • TF (Transform) frames and robot state publisher

Activities​

  • Create a simple publisher and subscriber node
  • Implement a service client and server
  • Build a URDF model of a simple robot
  • Visualize your robot in RViz

Figure 3: ROS 2 node communication patterns showing publishers, subscribers, and message passing.

Resources​

  • ROS 2 node creation tutorials
  • URDF tutorials and examples
  • TF and robot state publisher documentation

Estimated Time​

10-12 hours of study and hands-on practice

Week 3: Gazebo/Simulation​

Learning Objectives​

  • Set up and use Gazebo for physics simulation
  • Create custom simulation environments
  • Implement sensor simulation for cameras, LiDAR, and IMU
  • Integrate simulation with ROS 2 for seamless development

Topics Covered​

  • Gazebo simulation environment setup
  • Physics properties and world building
  • Sensor simulation and plugins
  • ROS 2 integration with Gazebo
  • Creating custom models and environments

Activities​

  • Launch your first Gazebo simulation with ROS 2
  • Create a custom world for robot testing
  • Add sensors to your robot model in simulation
  • Implement sensor data processing in ROS 2 nodes

Figure 4: Gazebo simulation environment with physics properties and sensor integration.

Resources​

  • Gazebo Garden documentation
  • ros_gz_bridge tutorials
  • Sensor plugin documentation

Estimated Time​

10-12 hours of study and hands-on practice

Week 4: Digital Twin​

Learning Objectives​

  • Understand digital twin concepts in robotics
  • Use Unity for high-fidelity visualization
  • Create accurate virtual representations of physical robots
  • Implement bidirectional communication between real and virtual systems

Topics Covered​

  • Digital twin architecture and benefits
  • Unity integration with ROS 2
  • High-fidelity visualization techniques
  • Bidirectional data flow between physical and virtual systems
  • Synthetic data generation

Activities​

  • Set up Unity with ROS-TCP-Endpoint
  • Create a digital twin of your robot in Unity
  • Implement real-time synchronization between simulation and Unity
  • Generate synthetic training data using your digital twin

Figure 5: Digital twin architecture showing the relationship between physical robots, digital models, and simulation environments.

Resources​

  • Unity Robotics documentation
  • ROS-TCP-Endpoint tutorials
  • Digital twin best practices

Estimated Time​

10-12 hours of study and hands-on practice

Week 5: Isaac AI Brain​

Learning Objectives​

  • Use NVIDIA Isaac Sim for advanced robotics simulation
  • Implement Isaac ROS perception pipelines
  • Apply Visual Simultaneous Localization and Mapping (VSLAM)
  • Leverage GPU acceleration for AI workloads

Topics Covered​

  • NVIDIA Isaac Sim setup and architecture
  • Isaac ROS perception stack
  • VSLAM techniques and implementation
  • GPU acceleration for robotics AI
  • Navigation solutions using Nav2

Activities​

  • Install and configure NVIDIA Isaac Sim
  • Implement perception pipelines using Isaac ROS
  • Run VSLAM algorithms in Isaac Sim
  • Configure Nav2 for robot navigation

Figure 6: NVIDIA Isaac architecture showing simulation, perception, and navigation components.

Resources​

  • NVIDIA Isaac Sim documentation
  • Isaac ROS tutorials
  • Nav2 navigation stack documentation

Estimated Time​

12-15 hours of study and hands-on practice

Week 6: VLA Robotics​

Learning Objectives​

  • Implement Vision-Language-Action (VLA) system architectures
  • Integrate Large Language Models (LLMs) with robotic systems
  • Process voice commands using speech recognition tools
  • Translate natural language commands into robotic actions

Topics Covered​

  • Vision-Language-Action systems architecture
  • Integration of LLMs with robotics
  • Speech recognition and processing (Whisper)
  • Natural language to action mapping
  • Multimodal interfaces for robotics

Activities​

  • Set up a VLA system with vision and language components
  • Integrate an LLM with your robot's control system
  • Implement voice command processing
  • Create a language-driven robot control interface

Figure 7: Vision-Language-Action system architecture showing the flow from vision input, language understanding, to action execution.

Resources​

  • Hugging Face Transformers documentation
  • OpenAI API documentation (or open-source LLM alternatives)
  • Speech recognition libraries documentation

Estimated Time​

12-15 hours of study and hands-on practice

Week 7: Capstone Project​

Learning Objectives​

  • Integrate concepts from all previous modules
  • Implement a complete physical AI system
  • Demonstrate proficiency in ROS 2, simulation, AI, and VLA systems
  • Present and document your capstone project

Topics Covered​

  • Integration of all textbook concepts
  • Project planning and execution
  • Testing and validation of complex systems
  • Documentation and presentation of results

Activities​

  • Design and implement your capstone project
  • Integrate simulation, AI, and control components
  • Test your system in both simulated and (if available) real environments
  • Document and present your project results

Figure 8: Capstone project showing integration of all modules: ROS 2, Simulation, AI, and VLA systems.

Resources​

  • All resources from previous weeks
  • Project planning and documentation templates
  • Best practices for robotics system integration

Estimated Time​

15-20 hours of study and hands-on practice

Additional Notes​

  • Each week builds upon the previous week's knowledge and skills
  • Hands-on practice is essential for mastering robotics concepts
  • Students are encouraged to experiment and explore beyond the required activities
  • Additional resources and advanced topics are available in the Additional Materials section
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