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Module 4: Hands-On Lab

🎯 Lab Objectives​

Integrate all components into a working autonomous humanoid system.

🛠️ Complete System Setup​

# Install dependencies
pip install openai-whisper sounddevice openai

# Setup workspace
cd ~/humanoid_ws
colcon build
source install/setup.bash

📝 Launch Complete System​

Create launch/autonomous_system.launch.py:

from launch import LaunchDescription
from launch_ros.actions import Node

def generate_launch_description():
    return LaunchDescription([
        # Voice recognition
        Node(
            package='humanoid_vla',
            executable='voice_command_node',
            name='voice_command'
        ),
        
        # Cognitive planner
        Node(
            package='humanoid_vla',
            executable='cognitive_planner',
            name='planner'
        ),
        
        # Action executor
        Node(
            package='humanoid_vla',
            executable='action_executor',
            name='executor'
        ),
        
        # Navigation
        Node(
            package='nav2_bringup',
            executable='navigation_launch.py',
            name='navigation'
        ),
    ])

🚀 Run the System​

# Terminal 1: Launch Isaac Sim
./isaac-sim.sh

# Terminal 2: Launch ROS 2 system
ros2 launch humanoid_vla autonomous_system.launch.py

# Terminal 3: Monitor
ros2 topic echo /robot_action

🎯 Test Commands​

Try these voice commands:

  • "Move forward 2 meters"
  • "Find the red box"
  • "Pick up the object"
  • "Clean the room"
  • "Bring me the cup"

📊 Expected Results​

  • ✅ Voice commands transcribed accurately
  • ✅ LLM generates logical action plans
  • ✅ Robot executes plans successfully
  • ✅ Complete tasks autonomously

🏆 Course Complete!​

Congratulations! You've mastered:

  • ROS 2 fundamentals
  • Simulation with Gazebo and Unity
  • NVIDIA Isaac platform
  • Vision-Language-Action systems

You're now ready to build the future of Physical AI and Humanoid Robotics! 🤖🚀

📚 Further Learning​

  • Explore advanced manipulation techniques
  • Study bipedal locomotion algorithms
  • Implement multi-robot coordination
  • Deploy to real hardware

Thank you for completing this course!