Nao Learning From Demonstrations

Welcome to the Nao Learning From Demonstrations repository! This project integrates robotic control, machine learning, and simulation environments for real-world and virtual robotic tasks. From motion prediction to grasping and pushing simulations, this repository provides all the necessary tools and scripts.

---

Project Overview

This repository is organized into several modules and folders, each designed to handle specific aspects of robotic control and simulation:

• KinectSkeleton: Processes skeleton data captured from Kinect sensors for motion mapping.
• Notebooks: Jupyter notebooks for training machine learning models.
• PostProcessing: Prepares and cleans datasets for training and evaluation.
• RealWorldScripts: Integrates models and robotic control for real-world tasks.
• Simulation: Scripts and scene files for simulating robotic interactions in CoppeliaSim.

---

Features

🎯 Motion Prediction

• Train machine learning models to predict robotic arm movements.
• Supports tasks like grasping, pushing.

🛠️ Simulation Integration

• CoppeliaSim scenes for realistic simulations.
• Includes grasping and pushing tasks with detailed configurations.

📊 Data Processing and Training

• Scripts for cleaning, interpolating, and labeling data.
• Jupyter notebooks for model development and evaluation.

🌐 Real-World Deployment

• Supports integration with robotic arms and Kinect sensors.
• Endpoints for model predictions and robotic control.

---

Getting Started

1. Clone the Repository:

   git clone https://github.com/TaarLab/NaoLfD.git
   cd NaoLfD

2. Set Up Dependencies:

   • Install Python requirements:

     pip install -r requirements.txt

   • Install CoppeliaSim.

3. Explore Modules:

   • Train models in Notebooks or process data in PostProcessing.

4. Real-World Integration:

   • Set up Kinect sensors or robotic arms and run RealWorldScripts.