Firas Zoghlami

About

Firas Zoghlami is a leading researcher in autonomous robotics and artificial intelligence, focusing on developing intelligent systems for industrial automation and logistics. His work centers on creating AI-driven methodologies that enable robots to operate flexibly and safely in dynamic, unstructured environments, such as production plants and warehouses. Zoghlami’s major contributions include the Q-Model, a pioneering AI-based framework for autonomous robot development, which has garnered 14 citations, and the AI Motion Control approach, which generates adaptive control policies for robotic manipulation tasks (10 citations). He has also advanced safety-critical learning with his AI-based learning approach for depalletization robots (8 citations) and introduced anomaly detection and deep post-gripping perception frameworks (7 and 6 citations, respectively) to enhance robotic awareness and reliability. His work on perception-based material handling and unsupervised pose anomaly detection further underscores his impact, with cumulative citations exceeding 60. Zoghlami’s research bridges the gap between theoretical AI and practical industrial applications, making him a key figure in the evolution of autonomous, intelligent robotic systems for modern manufacturing.

Research Focus

Key Achievements

Top Papers

1. 1
   Q-Model: An Artificial Intelligence Based Methodology for the Development of Autonomous Robots

   14 citations · 2020
2. 2
   Ai Motion Control – A Generic Approach to Develop Control Policies for Robotic Manipulation Tasks

   10 citations · 2019
3. 3
   AI-Based Learning Approach with Consideration of Safety Criteria on Example of a Depalletization Robot

   8 citations · 2019
4. 4
   Usage Identification of Anomaly Detection in an Industrial Context

   7 citations · 2019
5. 5
   Design of a Deep Post Gripping Perception Framework for Industrial Robots

   6 citations · 2020
6. 6
   Perceptionbased Intelligent Materialhandling in Industrial Logistics Environments

   4 citations · 2019
7. 7
   Unsupervised Pose Anomaly Detection for Dynamic Robotic Environments

   4 citations · 2020
8. 8
   Robust Framework for intelligent Gripping Point Detection

   3 citations · 2019
9. 9
   Reinforcement Learning Lifecycle for the Design of Advanced Robotic Systems

   3 citations · 2020
10. 10
    Enabling Robot Selective Trained Deep Neural Networks for Object Detection Through Intelligent Infrastructure

    2 citations · 2019

Key Collaborators

Contact & Links