Amin Ul Haq

University of Electronic Science and Technology of China

Papers

Total Citations

H-Index

About

Amin Ul Haq’s research lies at the intersection of robotics, human-robot interaction, and computer vision, with a focus on enabling intelligent, cooperative systems. His work on the “Partial Observer Decision Process Model for Crane-Robot Action” (2020, 10 citations) addresses a critical challenge in human-robot collaboration: predicting subsequent actions from present behaviors to streamline cooperative tasks. This model reduces human effort while improving task efficiency, a key contribution to the field of interactive robotics. More recently, Haq introduced “WallNet” (2024, 5 citations), a hierarchical visual attention-based model designed to detect putty bulge terminal points—a specialized but impactful application in construction or manufacturing quality control. By leveraging attention mechanisms, WallNet enhances precision in visual inspection tasks. Though his citation counts are modest, Haq’s work demonstrates a clear trajectory toward practical, real-world robotic systems that adapt to human partners. His research is particularly valuable for students and engineers interested in decision-making under partial observability and vision-guided automation.