Papers
116
Total Citations
3,795
H-Index
36
About
Giancarlo Ferrigno is a prominent researcher in medical robotics, human-robot interaction, and intelligent control systems, whose work has fundamentally advanced the field of robot-assisted surgery. Based at the Politecnico di Milano, Ferrigno has made landmark contributions to minimally invasive surgical robotics, particularly in developing collaborative control schemes for teleoperated systems. His highly cited 2016 paper on haptics in robot-assisted surgery (246 citations) laid critical groundwork for understanding force feedback challenges in surgical settings, while his 2019 work on redundant robot control for minimally invasive surgery (229 citations) introduced hierarchical operational space formulations that elegantly solve remote center of motion constraints. Ferrigno has championed the integration of deep neural networks into surgical robotics, applying machine learning to tool dynamics identification, EMG-based force estimation, and human-like redundancy optimization for anthropomorphic manipulators. His 2021 contribution on teaching-by-demonstration frameworks (217 citations) reflects his commitment to intelligent, adaptive surgical systems. With research spanning stereotactic neurosurgery robotics, bilateral teleoperation, and gesture-based human-robot interfaces, Ferrigno's cumulative citation impact exceeds 1,500 references, establishing him as a defining voice in next-generation surgical and rehabilitation robotics.
Research Focus
Key Achievements
Top Papers
- 1Haptics in Robot-Assisted Surgery: Challenges and Benefits246 citations · 2016
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- 3Toward Teaching by Demonstration for Robot-Assisted Minimally Invasive Surgery217 citations · 2021
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- 7Review of Robotic Technology for Stereotactic Neurosurgery105 citations · 2015
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