Eleonora Tagliabue
Papers
15
Total Citations
236
H-Index
7
About
Eleonora Tagliabue is a pioneering researcher at the intersection of surgical robotics, machine learning, and biomechanics, with a focus on developing autonomous and semi-autonomous systems for robot-assisted surgery. Her work addresses one of the field's most pressing challenges: enabling robotic systems to safely and intelligently interact with soft, deformable biological tissues. Tagliabue's most influential contributions include the development of simulation environments for training reinforcement learning agents in surgical contexts, garnering 71 citations, and advancing sim-to-real transfer techniques for visuomotor control of deformable objects, cited 66 times. These works have significantly accelerated research into surgical task automation by bridging the critical gap between simulated training and real-world deployment. Her research further extends into data-driven tissue dissection, biomechanically informed tissue retraction, and autonomous breast biopsy systems with deformation compensation, demonstrating a remarkably broad applied scope. More recently, Tagliabue has expanded into ophthalmic robotics, developing OCT-enhanced platforms and adaptive eye models for vitreoretinal surgery. Her 2023 opinion piece advocating for "common sense" reasoning in autonomous surgical systems reflects her broader vision for clinically meaningful AI. With over 220 cumulative citations, her work is shaping the future of intelligent, human-centered surgical automation.
Research Focus
Key Achievements
Top Papers
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- 5Autonomous Robotic System for Breast Biopsy With Deformation Compensation11 citations · 2023
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