Daniele De Gregorio
Papers
17
Total Citations
360
H-Index
9
About
Daniele De Gregorio is a robotics and computer vision researcher whose work spans human-robot interaction, robotic manipulation, tactile sensing, and neural scene representation. He has made significant contributions to the development of intuitive control strategies for robotic systems, most notably through his highly cited 2018 work on sEMG-based human-robot interfaces that leverage machine learning and synergies to enable natural teleoperation of robotic hands (115 citations). Equally impactful is his research integrating robotic vision with tactile sensing for precise manipulation tasks, such as wire-terminal insertion in industrial settings (93 citations), reflecting his sustained engagement with automation challenges in manufacturing through projects like the WIRES experiment. De Gregorio has also advanced deformable linear object handling, combining computer vision and recurrent neural networks for assembly applications. More recently, he has expanded into neural rendering, introducing the ReNe dataset to address relighting within Neural Radiance Fields (37 citations), demonstrating his ability to bridge classical robotics with cutting-edge 3D scene understanding. His diverse portfolio — encompassing path planning, semantic mapping, and multi-view reconstruction — underscores a research philosophy oriented toward building intelligent, perception-aware robotic systems capable of operating robustly in complex real-world environments.
Research Focus
Key Achievements
Top Papers
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- 4The WIRES Experiment: Tools and Strategies for Robotized Switchgear Cabling16 citations · 2017
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- 7DLO-in-Hole for Assembly Tasks with Tactile Feedback and LSTM Networks13 citations · 2019
- 8Automatized Switchgear Wiring: An Outline of the WIRES Experiment Results12 citations · 2019
- 9SkiMap++: Real-Time Mapping and Object Recognition for Robotics10 citations · 2017
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