Daniele De Greogrio
Papers
1
Total Citations
46
H-Index
1
About
Daniele De Gregorio is a leading researcher in robotic perception and manipulation, with a focus on the challenging domain of deformable linear objects. His key research areas span computer vision, deep learning, and robotic interaction, where he tackles the open problem of enabling robots to perceive and handle wires—a ubiquitous yet notoriously difficult task due to their lack of rigid structure. De Gregorio’s most notable contribution is the development of Ariadne+, a deep learning–based augmented framework for the instance segmentation of wires. This innovative algorithm, detailed in his 2022 paper, addresses the critical gap in robotic systems’ ability to identify and track individual wires in complex manufacturing environments. With 46 citations, this work has already garnered significant attention, highlighting its impact on advancing automation in industries reliant on cable harnessing and assembly. De Gregorio’s achievements demonstrate a rare blend of theoretical rigor and practical application, positioning him as a key figure in bridging the gap between deep learning and real-world robotic challenges. His research continues to inspire new approaches to perceiving and manipulating the world’s most flexible objects.
Research Focus
Key Achievements
Top Papers
- 1