Abdulqader Abusafieh
Papers
5
Total Citations
61
H-Index
4
About
Abdulqader Abusafieh is pioneering the fusion of vision-based tactile sensing and neuromorphic vision to push the boundaries of precision robotic manufacturing. His research centers on developing multi-functional sensors that enable cobots to achieve the high accuracy required for demanding applications like aerospace machining. Abusafieh’s most cited work introduces a novel vision-based sensor for simultaneous normality and position measurements, a breakthrough that directly addresses the precision gap in collaborative robotics. He further advances the field with TactiGraph, an asynchronous graph neural network that leverages neuromorphic vision-based tactile sensing to predict contact angles, achieving 18 citations and demonstrating how event-driven data can enhance robotic tactile feedback. His virtual prototyping of vision-based tactile sensors for robotic-assisted precision machining (10 citations) offers a cost-effective design methodology for industrial deployment. With over 60 total citations across his publications, including the recent E-POSE dataset for event camera-based object pose estimation, Abusafieh is establishing himself as a key innovator at the intersection of tactile sensing, computer vision, and intelligent manufacturing. His work promises to make high-precision robotic automation more accessible and reliable.
Research Focus
Key Achievements
Top Papers
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- 5E-POSE: A Large Scale Event Camera Dataset for Object Pose Estimation4 citations · 2025