Ilaria Burzo
Papers
2
Total Citations
46
H-Index
2
About
Ilaria Burzo is a rising researcher at the intersection of robotics, control systems, and medical technology, with a primary focus on advancing minimally invasive surgery (MIS) and medical contact tasks. Her work centers on developing intelligent, adaptive robotic systems that can safely and precisely interact with human tissue. Burzo’s most impactful contribution is an optimization-based variable impedance control strategy for robotic manipulators, which uses online Quadratic Programming to dynamically adjust robot stiffness during medical contact tasks—a paper that has already garnered 32 citations since its 2024 publication. She has also pioneered an Augmented Reality-assisted robot learning framework for MIS, integrating external optical tracking with Gaussian Mixture Models and Regression to enable robots to learn complex surgical tasks from human demonstration. This work, cited 14 times, bridges the gap between human expertise and robotic precision. By combining real-time optimization with learning from demonstration, Burzo is helping to create safer, more capable surgical robots. Her research is particularly notable for its practical, application-driven approach, directly addressing the challenges of compliance and safety in robot-assisted surgery.
Research Focus
Key Achievements
Top Papers
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