Marcel Lahoud

About

Marcel Lahoud is a researcher at the forefront of robotic manipulation, specializing in the identification and control of robotic systems and the automation of soft material manufacturing. His work addresses critical challenges in both rigid and flexible material handling, with a focus on enhancing precision and efficiency. Lahoud’s major contributions include developing a deep learning framework for non-symmetrical Coulomb friction identification in robotic manipulators, a method that significantly improves the accuracy of dynamic models for high-performance control systems—a paper that has garnered 8 citations since 2024. He has also pioneered robotic systems for soft materials, notably a manipulation system for multi-layer fabric stitching (5 citations) and a wrinkle-free sewing approach (3 citations), tackling the inherent difficulties of textiles’ non-linear behavior. Additionally, Lahoud contributed to the development of a flexible assembly system for the World Robot Summit 2020 Assembly Challenge, demonstrating his expertise in agile, high-mix/low-volume production. With a growing citation impact, his work bridges deep learning and robotics, promising transformative advances in manufacturing automation.

Research Focus

Key Achievements

Top Papers

1. 1
   A Deep Learning Framework for Non-Symmetrical Coulomb Friction Identification of Robotic Manipulators

   8 citations · 2024
2. 2
   Robotic Manipulation System for Multi-Layer Fabric Stitching

   5 citations · 2021
3. 3
   Wrinkle-Free Sewing with Robotics: The Future of Soft Material Manufacturing

   3 citations · 2023
4. 4
   Development of a Flexible Assembly System for the World Robot Summit 2020 Assembly Challenge

   2 citations · 2023

Key Collaborators

Contact & Links