Course Overview: Your Journey Through Physical AI & Humanoid Robotics
Introduction
This course is designed to take you from foundational concepts to building a fully autonomous humanoid robot in just 13 weeks. By combining four progressive modules with hands-on exercises and culminating in a capstone project, you'll gain practical skills in robot software development, simulation, perception, and AI integration.
The 4 Modules
Module 1: ROS 2 Fundamentals (Weeks 3-5)
Goal: Master the Robot Operating System 2 (ROS 2), the industry-standard middleware for robot software development.
What You'll Learn:
- ROS 2 architecture: nodes, topics, services, actions
- Publisher/subscriber communication patterns
- Creating custom message types
- Parameter management and configuration
- Launch files for complex robot systems
- ROS 2 command-line tools (ros2 topic, ros2 node, etc.)
- Writing Python and C++ ROS 2 nodes
- Time synchronization and tf2 (coordinate frame transforms)
Hands-On Projects:
- Build a sensor data publisher node
- Create a simple robot control system
- Implement a service-based task planner
- Develop a multi-node robot application with launch files
Why It Matters: ROS 2 is used in 90%+ of modern robotics projects, from research to commercial products. Companies like Boston Dynamics, Tesla, and Waymo all use ROS or ROS 2 derivatives.
Time Investment: 15-20 hours
Module 2: Simulation Environments (Weeks 6-8)
Goal: Learn to build and test robot systems in simulation before deploying to real hardware.
What You'll Learn:
- Gazebo Classic: Open-source robot simulator
- World creation and environment design
- URDF/SDF robot model descriptions
- Physics engine configuration
- Sensor simulation (cameras, LIDAR, IMU)
- Plugin development for custom behaviors
- Unity Robotics: Game engine for photorealistic simulation
- Unity-ROS integration
- Synthetic data generation for ML training
- Domain randomization techniques
- High-fidelity rendering for vision systems
Hands-On Projects:
- Create a custom robot model in URDF
- Build a warehouse environment in Gazebo
- Simulate sensor data (LIDAR, cameras, IMU)
- Train a simple navigation policy in Unity
Why It Matters: Simulation enables safe, fast, and scalable robot development. Test dangerous scenarios, generate infinite training data, and iterate 100x faster than real hardware.
Time Investment: 15-20 hours
Module 3: NVIDIA Isaac Sim & Isaac ROS (Weeks 9-10)
Goal: Harness GPU-accelerated simulation and perception using NVIDIA's Isaac platform.
What You'll Learn:
- Isaac Sim: Photorealistic, physics-accurate robot simulation
- GPU-accelerated physics (10-100x faster than CPU)
- Synthetic data generation for deep learning
- Integration with Omniverse for collaborative development
- Humanoid robot models and environments
- Isaac ROS: GPU-accelerated perception algorithms
- Visual SLAM (Simultaneous Localization and Mapping)
- Object detection and segmentation
- Depth estimation from stereo cameras
- Integration with ROS 2 navigation stack (Nav2)
Hands-On Projects:
- Deploy a humanoid robot in Isaac Sim
- Implement GPS-to-Nav2 conversion
- Run object detection with Isaac ROS DNN inference
- Navigate a complex environment with Nav2 and Isaac ROS perception
Why It Matters: NVIDIA Isaac is the cutting edge of robot simulation and perception, enabling real-time performance that was impossible just years ago. Used by leading robotics companies for development and testing.
Time Investment: 10-14 hours
Module 4: Vision-Language-Action (VLA) Integration (Weeks 11-12)
Goal: Integrate large language models (LLMs) with robot perception and control for natural language task understanding.
What You'll Learn:
- Voice Recognition: OpenAI Whisper for speech-to-text
- Audio capture and preprocessing
- ROS 2 integration for voice commands
- Multi-language support
- Cognitive Planning: GPT-4 for high-level task decomposition
- Prompt engineering for robotics
- Task decomposition ("get me a coffee" → navigation + manipulation steps)
- Safety validation and constraint checking
- Re-planning on failure
- Action Translation: Converting LLM outputs to ROS 2 actions
- Natural language → Nav2 goals
- Grounding abstract references ("the red mug") to specific objects
- Executing action sequences
- Complete VLA Pipeline: Voice command → task plan → robot execution
Hands-On Projects:
- Implement Whisper integration for voice control
- Create GPT-4 planning node with prompt engineering
- Build action translation layer (language → ROS 2 actions)
- Deploy complete VLA system on simulated humanoid
Why It Matters: VLA represents the future of human-robot interaction, enabling non-experts to command robots using natural language. This is how Tesla Optimus, Figure 01, and other next-gen humanoids will be controlled.
Time Investment: 12-16 hours
13-Week Course Structure
| Week | Module | Focus | Key Deliverables |
|---|---|---|---|
| 1-2 | Introduction | Physical AI concepts, sensor fundamentals | Chapter 1 completion, self-assessment |
| 3 | ROS 2 | Nodes, topics, pub/sub | Simple publisher/subscriber nodes |
| 4 | ROS 2 | Services, actions, parameters | Service-based control system |
| 5 | ROS 2 | Launch files, tf2, integration | Multi-node robot application |
| 6 | Simulation | Gazebo basics, URDF modeling | Custom robot model in Gazebo |
| 7 | Simulation | Sensor simulation, world creation | Warehouse environment with sensors |
| 8 | Simulation | Unity integration, synthetic data | Unity-based vision training dataset |
| 9 | Isaac Sim & ROS | Isaac Sim fundamentals, GPU physics | Humanoid robot in Isaac Sim |
| 10 | Isaac Sim & ROS | Isaac ROS perception, Nav2 | Object detection + navigation |
| 11 | VLA | Whisper + GPT-4 integration | Voice-controlled task planning |
| 12 | VLA | Complete VLA pipeline | End-to-end voice → action system |
| 13 | Capstone | Autonomous humanoid project | Final capstone demo |
Capstone Project: Autonomous Humanoid Robot (Week 13)
Project Goal: Build and deploy a complete autonomous humanoid robot system that:
- Understands voice commands using OpenAI Whisper
- Plans high-level tasks using GPT-4 (e.g., "Go to the kitchen and bring me a water bottle")
- Navigates autonomously using Isaac ROS perception and Nav2
- Manipulates objects using vision-guided grasping
- Operates in Isaac Sim with a realistic humanoid model
Required Capabilities:
- Multi-room navigation with dynamic obstacle avoidance
- Object detection and recognition
- Natural language task understanding
- Whole-body motion planning
- Failure recovery and re-planning
Example Task: "I'm thirsty. Can you get me a drink from the kitchen?"
System Response:
- Whisper transcribes voice command
- GPT-4 decomposes task: [navigate to kitchen] → [locate beverage] → [grasp object] → [navigate to user] → [hand over]
- Robot executes each action using Nav2, Isaac ROS perception, and manipulation controllers
- System provides voice feedback on progress
Evaluation Criteria:
- Voice command understanding accuracy (>80%)
- Navigation success rate (>90% for known environments)
- Task completion success (>70% for multi-step tasks)
- Safety (no collisions, proper error handling)
- Code quality and documentation
Time Investment: 15-20 hours for capstone
Prerequisites
Before starting this course, you should have:
-
Programming Skills:
- Proficiency in Python (functions, classes, async/await)
- Basic C++ helpful but not required
- Familiarity with Linux command line
-
AI/ML Background:
- Introductory machine learning concepts (supervised learning, neural networks)
- Familiarity with PyTorch or TensorFlow is helpful but not required
-
Math Background:
- Linear algebra (vectors, matrices, transformations)
- Basic calculus (derivatives, optimization)
- Probability and statistics fundamentals
-
Hardware Requirements:
- Minimum: Ubuntu 22.04, 16GB RAM, 4-core CPU, 50GB disk
- Recommended: Ubuntu 22.04, 32GB RAM, 8-core CPU, NVIDIA GPU (RTX 3060+), 200GB SSD
- For Isaac Sim: NVIDIA GPU with 8GB+ VRAM required
Learning Approach
Self-Paced Study
- All materials available for independent learning
- Estimated 50-70 hours total time investment
- Self-assessment quizzes after each module
- Discussion forum for peer support
Instructor-Led Format
- Weekly live sessions covering key concepts
- Office hours for Q&A
- Peer code reviews
- Graded assignments and capstone presentation
Hybrid Model
- Pre-recorded lectures for core content
- Live labs for hands-on practice
- Slack/Discord for ongoing discussions
Tools & Technologies
You'll gain hands-on experience with industry-standard tools:
- ROS 2 Humble (latest LTS release)
- Python 3.10+ and C++17
- Gazebo Classic and Unity 2022+
- NVIDIA Isaac Sim 2023+
- Isaac ROS perception packages
- OpenAI API (Whisper, GPT-4)
- Git for version control
- Docker for containerized development
Career Preparation
Upon completing this course, you'll be prepared for roles such as:
- Robotics Software Engineer: Develop perception, planning, and control systems
- Embodied AI Engineer: Integrate LLMs with physical systems
- Simulation Engineer: Build training environments and synthetic data pipelines
- Research Engineer: Contribute to academic or industry research in Physical AI
Portfolio Project: Your capstone autonomous humanoid is a production-ready portfolio piece demonstrating end-to-end Physical AI development.
Summary
- 4 Modules: ROS 2 → Simulation → Isaac → VLA
- 13 Weeks: Progressive curriculum from fundamentals to advanced integration
- Hands-On Focus: Every module includes practical projects
- Capstone Project: Autonomous voice-controlled humanoid robot
- Career Ready: Portfolio project + industry-standard tool expertise
- Time Investment: 50-70 hours total (self-paced) or 13 weeks (instructor-led)
Review Questions
-
Which module teaches the industry-standard middleware for robot software?
- A) Module 2: Simulation
- B) Module 1: ROS 2
- C) Module 3: Isaac
- D) Module 4: VLA
-
What is the primary benefit of using simulation in robotics development?
- A) It's more fun than real robots
- B) Safe, fast, and scalable testing without hardware risks
- C) Simulations are always 100% accurate
- D) You don't need to learn ROS 2
-
What does the Vision-Language-Action (VLA) module enable?
- A) Better camera quality
- B) Natural language task understanding and control
- C) Faster robot movements
- D) Reduced hardware costs
-
What is the goal of the Week 13 capstone project?
- A) Write a research paper
- B) Build an autonomous humanoid that understands voice commands and executes tasks
- C) Pass a written exam
- D) Purchase a real humanoid robot
-
Which NVIDIA platform provides GPU-accelerated robot simulation?
- A) CUDA
- B) TensorRT
- C) Isaac Sim
- D) GeForce Experience
Answers: 1-B, 2-B, 3-B, 4-B, 5-C
Next Steps
Continue to Section 6: Self-Assessment to test your understanding of all Chapter 1 concepts before moving to Module 1.