Milad Rabiei

I am Master's student of Robotics Engineering at Università degli Studi di Genova, Italy, supervised by Arash Ajoudani and Fulvio Mastrogiovanni.
I received my BSc in Electrical Engineering at Shahid Beheshti University, Iran, under the supervision of Mohammad Hossein Moaiyeri, working with my peers in SBU’s Robotics and Intelligent Automation Lab (Auriga).
A livello personale, mi interessa esplorare nuovi hobby, viaggiare e apprendere continuamente ;).

Email  /  CV  /  Scholar  /  LinkedIn  /  GitHub

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Research/Publications

Interests: embodied cognition, neurosymbolic AI, deep learning, and their applications for robot learning.
Mutual Impact of Feature Selection and Privacy-preserving Mechanisms

Mina Alishahi, Vahideh Moghtadaiee, Amir Fathalizadeh, Milad Rabiei
International Journal of Machine Learning and Cybernetics, 2024-5
Springer

Differentially Private GANs for Generating Synthetic Indoor Location Data

Vahideh Moghtadaiee, Mina Alishahi, Milad Rabiei
International Journal of Information Security, 2024-5
Springer

An Optimized Density-Based Lane Keeping System for A Cost-Efficient Autonomous Vehicle Platform

Farbod Younesi, Milad Rabiei, Soroosh Keivanfard, Mohsen Sharifi, Marzieh Ghayour, Bahar Moadeli, Arshia Jafari, Mohammad Hossein Moaiyeri
Manuscript, 2023
ArXiv

Teaching
Shahid Beheshti University   |   Head Teaching Assistant 2021 – 2024
  • Machine Learning   Fall 2023, Spring 2024
    Instructor: Reza Ghaderi

  • Introduction to Artificial Intelligence   Spring 2023
    Instructor: Atefe Aghaei

  • Computer Programming   Spring 2021, Fall 2022
    Instructor: Vahideh Moghtadaiee

RoboCamp   |   Instructor
Danesh High School   |   Instructor
  • Programming Fundamentals   Summer 2024

Projects
Review: Imitation Learning through Action Understanding
Paper

(course project) Literature review of action modeling with imitation learning for robotic manipulation (IEEE 20-25)
Task Priority Inverse Kinematics in Simulation
Code

Task Priority Kinematic Control of an Underwater Vehicle Manipulation System (UVMS) and Bimanipulation.
Warehouse Automation with PDDL+
Code

A PDDL+ planning domain for 5 warehouse automation scenarios involving robots loading crates and conveyor belts.
2D Drone Simulation with FastDDS
Code

Terminal-based interactive drone simulator with obstacle avoidance. Supports local and remote object interaction via FastDDS.
Cartpole Q-Learning
Code

The CartPole reinforcement learning task using Q-learning from scratch.
1:10 Scale Self-Driving Robot Car
Youtube

Developed end-to-end autonomous driving system including lane detection, traffic sign recognition, and synthetic data augmentation. Deployed deep models in real-time.
Face and Hand Gesture Detection
Code

Built a gesture recognition system using Mediapipe landmarks and neural networks for sign language detection. Real-time output sent to ESP32 hardware.
Image Recovery with Genetic Algorithm
Code

Reconstructed an image of Alan Turing using a custom genetic algorithm with enhanced mutation and crossover techniques on random pixel arrays.
Sobel Edge Detection in VHDL
Code

Implemented Sobel edge detection on FPGA using VHDL. Python used for pixel parsing and integration. Simulated in Xilinx ISE.
Movie Recommendation System
Code

CLI-based C++ recommender system using collaborative filtering and cosine similarity. Works on dynamic user-movie rating datasets.
GAN / CGAN on MNIST

Synthetic handwritten digit generation using GAN and Conditional GAN architectures.
Ball Balancer Bot
Description

Designed a PID-controlled system using ESP8266 and ToF sensors to stabilize a ball on a rod. Supports remote setpoint control via web interface.

Miscellanea

Amazing reads:

  • A PhD Is Not Enough – Peter Feibelman
  • Crafting Your Research Future – Charles Ling, Qiang Yang
  • Surely You're Joking, Mr. Feynman – Richard Feynman
  • AI: Great Expectations (1988, one-page note on AI) – Rodney Brooks
  • A Mathematical Introduction to Robot Manipulation – Murray, Sastry, Li
  • Probabilistic Robotics – Thrun, Burgard, Fox
  • Grokking Deep Reinforcement Learning – Miguel Morales
  • Reinforcement Learning: An Introduction – Barto, Sutton
  • An Introduction to Quantum Computing – Kaye, Laflamme, Mosca
  • Physical Mathematics – Michael P. Brenner
  • Artificial Intelligence: A Modern Approach – Norvig, Russell
  • Deep Learning – Courville, Goodfellow, Bengio
  • Information Theory: From Coding to Learning – Wu, Polyanskiy
  • Data Science from Scratch – Joel Grus
  • The Road to Reality – Roger Penrose
  • Beej's Guide to C Programming –

Design and source inspired from Jon Barron's. Last updated June 2026.