Francesco Cursi
Papers
20
Total Citations
277
H-Index
11
About
Francesco Cursi is a robotics researcher whose work sits at the intersection of surgical robotics, machine learning-based modeling, and robot control. His research focuses primarily on minimally invasive surgical robots, with particular emphasis on tendon-driven flexible instruments, macro-micro manipulator systems, and kinematic modeling under uncertainty. Cursi has made significant contributions to addressing one of the central challenges in surgical robotics: the complex nonlinearities introduced by tendon-driven actuation mechanisms. To tackle these, he has pioneered the application of advanced learning-based approaches, including LSTM networks, Bayesian Neural Networks, and augmented neural architectures in SE(3), enabling more accurate and uncertainty-aware robot models. His work on hierarchical Model Predictive Control further demonstrates a commitment to translating these models into safe, precise clinical applications. Beyond surgical systems, Cursi has contributed to workspace analysis tooling and dual-arm coordinated manipulation. His most cited works span optimization of surgical instrument mounting (29 citations), workspace visualization (28 citations), and task-based kinematic learning (26 citations), reflecting broad impact across the robotics community. Collectively, his research advances the reliability and autonomy of next-generation surgical robotic systems.
Research Focus
Key Achievements
Top Papers
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- 5Model Learning With Backlash Compensation for a Tendon-Driven Surgical Robot20 citations · 2022
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- 7Augmented Neural Network for Full Robot Kinematic Modelling in SE(3)19 citations · 2022
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