Papers
5
Total Citations
183
H-Index
4
About
Fabien Despinoy’s research sits at the intersection of surgical robotics, human-machine interaction, and autonomous manipulation, with a focus on making robotic systems more intuitive and capable. His most influential work, “Unsupervised Trajectory Segmentation for Surgical Gesture Recognition in Robotic Training” (115 citations), pioneered methods to automatically analyze surgical dexterity and procedural knowledge—critical skills that current training systems struggle to assess. This contribution has shaped how robotic surgical training can provide objective, data-driven feedback. Despinoy also explores the frontier of autonomous robotics, as seen in his recent work on “Language-Grounded Dynamic Scene Graphs for Interactive Object Search With Mobile Manipulation” (50 citations), which leverages large language models to enable robots to execute complex, long-horizon tasks in unknown environments. His earlier studies on novel optical human-machine interfaces for laparoscopic telesurgery and eye-hand coordination for remote teleoperation further demonstrate his commitment to improving teleoperation fidelity. With a career spanning gesture recognition, surgical workflow analysis, and language-guided robotics, Despinoy’s work is driving progress toward more intelligent, responsive robotic systems for both clinical and autonomous applications.
Research Focus
Key Achievements
Top Papers
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- 4MIcro-surgical anastomose workflow recognition challenge report5 citations · 2021
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