Mohamad A. Alawad

About

Mohamad A. Alawad is at the forefront of intelligent systems, pioneering research at the intersection of robotics, control theory, and computer vision. His work is defined by a dual focus: advancing the autonomy of complex mechanical systems and enhancing machine perception from a first-person viewpoint. Alawad’s major contribution includes the application of Deep Reinforcement Learning (DRL) to control cable-driven parallel robots, a challenging domain where traditional control methods fall short. By leveraging DRL, he has developed robust control strategies that operate without explicit process models, marking a significant paradigm shift in robotic manipulation. In parallel, Alawad addresses the critical challenges of egocentric vision with his novel YOLO-ViT hybrid architecture, "EgoVision," which dramatically improves object recognition under difficult conditions like occlusion. While his most-cited works are recent (2025), their immediate impact is clear, with his DRL paper already garnering 2 citations and his vision paper 1 citation, signaling strong interest from the research community. Alawad’s work is not only technically innovative but also highly practical, with direct implications for assistive technologies, augmented reality, and human-robot collaboration.

Research Focus

Key Achievements

Top Papers

1. 1
   Controlling Cable Driven Parallel Robots Operations—Deep Reinforcement Learning Approach

   2 citations · 2025
2. 2
   EgoVision a YOLO-ViT hybrid for robust egocentric object recognition

   1 citations · 2025

Key Collaborators

Contact & Links