| Project Domains | Mentors | Project Difficulty |
|---|---|---|
| Reinforcement Learning, Python Programming, Robotics Simulation | Akash Kawle, Ansh Semwal | Hard |
Project Description
This project will introduce students to physical systems based RL-policy networks and logic solvers by building a system that can solve a Rubik’s Cube. Starting with a 2x2 cube and progressing to the standard 3x3, students will develop a Deep Reinforcement Learning (DeepRL) agent capable of learning optimal sequences of moves to solve the cube from any scrambled state.
The learned move sequences will be executed by a low-cost robotic setup driven by stepper motors and controlled via an ESP32 microcontroller. Along the way, students will gain hands-on experience in Python, RL frameworks, reward engineering, computer vision for cube state recognition, and embedded systems programming.
By the end of the project, students will have a working pipeline that connects decision-making AI with real-world actuation—a complete software-to-hardware system.