Physical AI Humanoid Robotics

A comprehensive AI-powered humanoid robotics project implementing advanced control algorithms, computer vision, and machine learning for autonomous humanoid robot behavior.

Table of Contents

• Overview
• Features
• Tech Stack
• Installation
• Usage
• Project Structure
• Contributing
• License

Overview

This project implements a complete AI-powered humanoid robotics system with:

• Advanced gait control and balance algorithms
• Computer vision for perception and navigation
• Reinforcement learning for adaptive behavior
• Real-time servo control and sensor integration
• Simulation environment for testing

Features

• Gait Control: Stable walking patterns with dynamic balance adjustment
• Computer Vision: Object detection, pose estimation, and environmental awareness
• AI Behavior: Machine learning models for decision making and adaptation
• Hardware Integration: Direct control of servos, sensors, and actuators
• Simulation: Gazebo-based physics simulation for development and testing
• ROS2 Compatibility: Integration with Robot Operating System 2

Tech Stack

Programming Languages

• Python 3.8+
• C++ (for performance-critical components)

Libraries & Frameworks

• Computer Vision: OpenCV, MediaPipe, PIL
• Machine Learning: TensorFlow, PyTorch, scikit-learn
• Robotics: ROS2 (Humble Hawksbill), MoveIt, Gazebo
• Mathematics: NumPy, SciPy, SymPy
• Visualization: Matplotlib, Plotly
• Communication: Socket programming, Serial communication

Hardware Support

• Servo motors (Dynamixel, SG90, etc.)
• IMU sensors (MPU6050, BNO055)
• Camera modules (USB, CSI)
• Microcontrollers (Arduino, Raspberry Pi)

Installation

Prerequisites

• Python 3.8 or higher
• Git
• CMake
• GCC/G++

Setup Instructions

1. Clone the repository:

git clone https://github.com/RohmaShabbir/physical-ai-humanoid-robotics.git
cd physical-ai-humanoid-robotics

2. Create a virtual environment:

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies:

pip install -r requirements.txt

4. Install ROS2 (if not already installed): Follow the official ROS2 Humble Hawksbill installation guide for your OS.

5. Set up the project:

# Build the project
python setup.py build_ext --inplace

# Or install in development mode
pip install -e .

Usage

Running the Main Controller

python src/main.py

Starting the Vision System

python vision/perception.py

Training AI Models

python ai_models/train_behavior.py

Running Simulations

# Start Gazebo simulation
ros2 launch simulation/launch_simulation.launch.py

# Run robot controller in simulation mode
python src/main.py --simulated

Project Structure

physical-ai-humanoid-robotics/
├── README.md
├── requirements.txt
├── .gitignore
├── LICENSE
├── config/
│   ├── robot_config.yaml          # Robot configuration parameters
│   └── vision_config.json         # Vision system settings
├── src/
│   ├── main.py                    # Main entry point
│   ├── robot_control.py           # High-level robot control
│   └── utils.py                   # Utility functions
├── controllers/
│   ├── humanoid_gait.py           # Walking pattern generation
│   ├── balance_controller.py      # Balance maintenance algorithms
│   └── trajectory_planner.py      # Path planning
├── vision/
│   ├── perception.py              # Main vision processing
│   ├── object_detection.py        # Object recognition
│   └── pose_estimation.py         # Human pose estimation
├── ai_models/
│   ├── behavior_model.py          # AI decision making
│   ├── rl_agent.py                # Reinforcement learning
│   └── neural_networks.py         # Custom neural networks
├── simulation/
│   ├── gazebo_interface.py        # Gazebo connection
│   └── physics_simulator.py       # Physics calculations
├── hardware/
│   ├── servo_control.py           # Servo motor control
│   ├── sensor_integration.py      # Sensor data processing
│   └── motor_drivers.py           # Motor driver interfaces
└── tests/
    └── test_robot.py              # Unit tests

Configuration

The project uses YAML and JSON configuration files located in the config/ directory:

• robot_config.yaml: Contains physical properties, servo limits, joint angles, etc.
• vision_config.json: Defines camera parameters, detection thresholds, etc.

Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Make your changes
4. Add tests if applicable
5. Commit your changes (git commit -m 'Add amazing feature')
6. Push to the branch (git push origin feature/amazing-feature)
7. Open a Pull Request

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• Inspired by Boston Dynamics, Honda ASIMO, and other humanoid robotics projects
• Based on open-source robotics frameworks and libraries
• Thanks to the ROS community for their excellent tools and documentation