Redhwan Algabri
Sungkyunkwan University, Hanyang University, Sejong University
Papers
10
Total Citations
168
H-Index
5
About
Redhwan Algabri is a robotics and artificial intelligence researcher whose work centers on human-robot interaction, autonomous mobile robots, and intelligent perception systems. He is best known for his pioneering contributions to deep-learning-based person following in mobile robots, developing novel frameworks that enable robots to reliably track individuals despite challenging real-world conditions such as occlusion, illumination changes, and visually similar targets. His 2020 paper on color-feature-driven indoor human following has garnered over 100 citations, establishing him as a notable voice in the field. Algabri extended this work through online trajectory prediction and adaptive color-based identification updates, addressing critical limitations in robust target recovery. More recently, his research has broadened into industrial robot fault diagnosis, employing advanced signal processing techniques such as Singular Spectrum Analysis to detect subtle mechanical defects. He has also contributed to accessible robotics hardware through open-source gripper design and the YAREN humanoid platform, reflecting a commitment to democratizing robotics research. His work on deep reinforcement learning for navigation and quaternion-based head pose estimation further demonstrates the breadth of his contributions, making him an influential figure bridging perception, autonomy, and physical robot systems.
Research Focus
Key Achievements
Top Papers
- 1Deep-Learning-Based Indoor Human Following of Mobile Robot Using Color Feature100 citations · 2020
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- 9WQuatNet: Wide range quaternion-based head pose estimation3 citations · 2025
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