Robotics and Embedded Systems Track - Robot Arm Project

Welcome to the Google Developer Groups on Campus Robotics and Embedded Systems Track at Covenant University. This repository showcases the ongoing development of a robotic arm capable of learning to pick and place objects.

---

Project Overview

Our goal is to build a robotic system that can:

• Detect and manipulate objects in a 3D space.
• Perform pick-and-place tasks autonomously using reinforcement learning and inverse kinematics.

We use PyBullet, Python, and matplotlib to simulate and visualize the robot arm's movements.

---

Installation

To run the simulation, ensure the following dependencies are installed:

• Python 3.12+
• PyBullet: pip install pybullet
• NumPy: pip install numpy
• matplotlib: pip install matplotlib

---

How to Run

1. Clone this repository:

   git clone https://github.com/iitimii/robot_arm.git
   cd robot-arm

2. Run the simulation:

   python arm_pick_nd_place.py

Features

Simulation Environment

• Physics Engine: PyBullet provides a realistic environment for simulating the robot's movements and interactions.

Visualization

• Joint angles plotted over time.
• 3D trajectory of the end effector.

3D Models

• Robot Arm Model (To be added).
• Objects Model objects to be picked and placed by the robotic arm. (To be added).

Hardware Code

• Microcontroller Interface (To be added).
• Motor Control (To be added).

Reinforcement Learning

• Task Learning using RL algorithms to optimize pick-and-place actions. (To be added).

---

Roadmap

Current Phase

• Implementing proof-of-concept pick-and-place tasks using the Kuka Robot arm model.

Next Steps

• Develop and integrate our custom arm model.
• Integrate camera for object detection.
• Integrate Gymnasium and Stable Baselines3 for reinforcement learning.
• Use reinforcement learning for dynamic task adaptation.
• Build a physical version of the robot.
• Implement a Vision Language Action model for improved task understanding and interaction.

---

Contributing

We welcome contributions! To get started:

1. Fork the repository.
2. Create a new branch for your feature: git checkout -b feature-name.
3. Commit your changes: git commit -m 'Add feature'.
4. Push to your branch: git push origin feature-name.
5. Create a pull request.

---

License

This project is licensed under the MIT License. See the LICENSE file for more details.

---

Contact

For questions or collaboration, feel free to reach out to the Robotics and Embedded Systems Track team:

• Lead: Timilehin Owolabi
• Email: timilehin.owolabi@stu.cu.edu.ng

---