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Chapter 5: Capstone Project Overview

Overview​

Welcome to Module 5! This is where you integrate everything you've learned across the previous modules into a comprehensive capstone project: The Autonomous Humanoid Robot.

Project Goal​

Build a simulated humanoid robot that can:

  1. Listen: Receive voice commands from a human
  2. Understand: Parse natural language using LLMs
  3. See: Perceive the environment using computer vision
  4. Plan: Generate a sequence of actions to accomplish the task
  5. Navigate: Move through the environment avoiding obstacles
  6. Manipulate: Pick up and place objects
  7. Report: Provide status updates to the user

Example Scenario​

User Command: "Please bring me the red cup from the kitchen table"

Robot Behavior:

  1. Processes speech using Whisper AI
  2. Uses GPT-4 to understand the task
  3. Plans navigation path to kitchen
  4. Uses visual SLAM to localize
  5. Identifies "red cup" using computer vision
  6. Plans grasping motion
  7. Picks up the cup
  8. Navigates back to user
  9. Hands over the cup
  10. Says "Here is your red cup"

System Architecture​

┌─────────────────────────────────────────────┐
│          Voice Interface Layer              │
│  (Whisper AI, Text-to-Speech)               │
└──────────────┬──────────────────────────────┘
               │
┌──────────────┴──────────────────────────────┐
│        Language Understanding Layer         │
│  (GPT-4, Task Planning)                     │
└──────────────┬──────────────────────────────┘
               │
┌──────────────┴──────────────────────────────┐
│         Perception Layer                    │
│  (SLAM, Object Detection, Depth)            │
└──────────────┬──────────────────────────────┘
               │
┌──────────────┴──────────────────────────────┐
│         Planning & Control Layer            │
│  (Nav2, MoveIt2, Trajectory Planning)       │
└──────────────┬──────────────────────────────┘
               │
┌──────────────┴──────────────────────────────┐
│         Simulation Layer                    │
│  (Isaac Sim, Gazebo)                        │
└─────────────────────────────────────────────┘

Required Components​

1. Robot Model​

  • Humanoid URDF with articulated joints
  • Stereo camera or RGB-D sensor
  • IMU for balance
  • Gripper for manipulation

2. Environment​

  • Kitchen scene with table, chairs, objects
  • Dynamic obstacles (e.g., moving people)
  • Realistic lighting and textures

3. Software Stack​

  • ROS 2 Humble
  • Isaac Sim or Gazebo
  • OpenAI API (GPT-4, Whisper)
  • Isaac ROS or standard ROS 2 packages

Modules Integration​

From Module 1 (ROS 2)​

  • Node architecture
  • Topic communication
  • Service calls
  • Action servers

From Module 2 (Gazebo)​

  • Robot simulation
  • Sensor simulation
  • World building
  • Physics tuning

From Module 3 (Isaac)​

  • Visual SLAM
  • Synthetic data generation
  • GPU-accelerated perception
  • Navigation stack

From Module 4 (VLA)​

  • Voice-to-text conversion
  • Natural language understanding
  • Vision-language grounding
  • Action generation

Project Milestones​

Milestone 1: Basic Setup (Week 1)​

  • Create humanoid robot model
  • Set up simulation environment
  • Verify ROS 2 integration

Milestone 2: Perception (Week 2)​

  • Implement visual SLAM
  • Add object detection
  • Integrate depth sensing

Milestone 3: Navigation (Week 3)​

  • Implement path planning
  • Add obstacle avoidance
  • Test bipedal locomotion

Milestone 4: Manipulation (Week 4)​

  • Implement inverse kinematics
  • Add grasping controller
  • Test pick-and-place

Milestone 5: Intelligence (Week 5)​

  • Integrate voice interface
  • Add LLM-based planning
  • Implement task execution

Milestone 6: Integration & Testing (Week 6)​

  • End-to-end system testing
  • Edge case handling
  • Performance optimization

Evaluation Criteria​

Your project will be evaluated on:

  1. Functionality (40%)

    • Does it accomplish the task?
    • How robust is the system?

  2. Integration (25%)

    • Are all components working together?
    • Is the architecture clean and modular?

  3. Innovation (20%)

    • Did you add novel features?
    • How creative is your solution?

  4. Documentation (15%)

    • Is the code well-documented?
    • Can others reproduce your work?

Getting Started​

Step 1: Choose Your Robot​

Options:

  • Custom humanoid: Design your own
  • Existing model: Use Boston Dynamics Atlas, Agility Digit
  • Simplified version: 2-wheeled base + arm

Step 2: Define Your Task​

Start simple, then expand:

  • Basic: Navigate to a location
  • Intermediate: Pick up a specific object
  • Advanced: Complete multi-step task

Step 3: Set Up Development Environment​

# Create workspace
mkdir -p ~/capstone_ws/src
cd ~/capstone_ws/src

# Clone templates
git clone https://github.com/your-org/capstone-template.git

# Build
cd ~/capstone_ws
colcon build
source install/setup.bash

Next Steps​

In the following chapters, we'll cover:

  • Detailed implementation guides
  • Troubleshooting common issues
  • Advanced features and extensions
  • Deployment considerations

Continue learning: Next Chapter → | Back to Module Overview

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