Mohammad Alkhatib
Centre National de la Recherche Scientifique, Institut Pascal
Papers
5
Total Citations
62
H-Index
4
About
Mohammad Alkhatib is an emerging robotics researcher whose work spans soft robotics, medical microrobotics, and human-robot interaction. His research has garnered significant attention in the scientific community, with his most impactful contributions already accumulating dozens of citations within just a few years of publication. Alkhatib has made notable advances in soft robotic grippers, developing a variable stiffness design incorporating magnetorheological fluids that enables reliable grasping of diverse objects — a paper that earned 19 citations since 2024. Equally impressive is his pioneering work in medical microrobotics, where he developed deep learning-based systems for fully automatic, real-time detection and tracking of microscale robots using ultrasound imaging, contributing both methodology and a dedicated benchmark dataset (USMicroMagSet) to accelerate future clinical translation — each garnering 18 citations. His research extends into robot learning and surgical assistance, including dual quaternion-based dynamic movement primitives for teleoperation-guided industrial task learning, and markerless ultrasound probe pose estimation for minimally invasive surgery. Collectively, Alkhatib's work bridges fundamental robotics with critical medical applications, positioning him as a promising contributor to the future of intelligent surgical systems and autonomous microrobotic healthcare technologies.
Research Focus
Key Achievements
Top Papers
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- 5Markerless Ultrasound Probe Pose Estimation in Mini-Invasive Surgery3 citations · 2024