Dario Zurlo
Papers
1
Total Citations
12
H-Index
1
About
Dario Zurlo is a leading researcher in physical human-robot interaction (pHRI), with a focus on making collaborative robots safer and more intuitive. His most-cited work, "Collision Detection and Contact Point Estimation Using Virtual Joint Torque Sensing Applied to a Cobot" (2023, 12 citations), introduces a complete framework for detecting, isolating, and reacting to contact forces in real time. Tested on a novel 6-degree-of-freedom industrial cobot, this research advances the ability of robots to sense and respond to physical interactions without expensive external sensors. By leveraging virtual joint torque sensing, Zurlo’s approach enables more reliable collision detection and precise contact localization—critical for safe human-robot collaboration. His contributions are shaping the next generation of adaptive, human-aware automation, with implications for manufacturing, assistive robotics, and beyond. Zurlo’s work stands out for its practical, hardware-validated solutions that bridge theory and real-world deployment, making him a key voice in the push toward truly collaborative robotics.
Research Focus
Key Achievements
Top Papers
- 1