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About

Kareem Eltouny is a researcher at the forefront of human-robot collaboration and intelligent manufacturing, with a focus on making industrial robots safer and more autonomous in unstructured environments. His work centers on three interconnected areas: uncertainty-aware perception, human motion prediction, and knowledge-informed robotic planning. Eltouny’s major contributions include developing deep learning systems that not only detect human workers and tools—such as his automatic screw detection and tool recommendation system for robotic disassembly (41 citations)—but also quantify the uncertainty in their predictions, enabling safer close collaboration. His DE-TGN framework (13 citations) advances human motion forecasting by using deep ensembles to predict worker movements with confidence estimates, directly informing collision-avoidance strategies. This uncertainty-aware approach is integrated into graph-based manipulator motion planning (8 citations), allowing robots to anticipate and adapt to human actions in real time. Most recently, his KG-Planner (7 citations) introduces a knowledge-informed graph neural planner that efficiently generates collision-free paths in dynamic, human-filled environments. Collectively, Eltouny’s work bridges the gap between perception, prediction, and planning, directly addressing the core challenge of safe, efficient human-robot collaboration in remanufacturing and beyond.