Papers
3
Total Citations
19
H-Index
3
About
Mohamed Tahoun is a robotics researcher whose work sits at the intersection of computer vision, tactile sensing, and 3D shape reconstruction for autonomous manipulation. His primary contributions focus on enabling robots to perceive and interact with objects more intelligently by fusing visual and tactile data. In his most cited work, “Visual-Tactile Fusion for 3D Objects Reconstruction from a Single Depth View and a Single Gripper Touch” (2021, 10 citations), Tahoun addresses a fundamental limitation of vision-only systems: the inability to see occluded sides of an object. By integrating a single tactile reading with a depth image, his method reconstructs complete 3D shapes, directly improving grasp planning. Earlier, he developed a low-cost force localized interaction sensor for the HYDROïD humanoid robot (2019, 6 citations), demonstrating a practical, deformable sensing system that can be embedded in robotic limbs. His 2019 work on visual completion of 3D shapes from a single view further advances perception for robotic tasks. Tahoun’s research is notable for its pragmatic, sensor-fusion approach—combining affordable hardware with robust mathematical models—making his contributions highly relevant for real-world robotic applications in grasping and manipulation.
Research Focus
Key Achievements
Top Papers
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- 2Force Localized Interaction Sensing System for HYDROïD Humanoid Robot6 citations · 2019
- 3