Mattia Zeni

Papers

Total Citations

H-Index

About

Mattia Zeni’s research sits at the intersection of robotics, artificial intelligence, and human-centered computing, with a core focus on enabling machines to understand and interact with their surroundings in a human-like manner. His major contributions center on developing context-aware models for robot surveillance, where he addresses the critical challenge of translating human situational awareness into robotic systems. By designing frameworks that allow robots to “sense” and “model” context as humans do, Zeni’s work enhances autonomous decision-making in real-world environments, particularly where robots must interact safely and intuitively with people. His most-cited papers, “Human-Like Context Modelling for Robot Surveillance” (2017) and “Human-Like Context Sensing for Robot Surveillance” (2018), each garnering 3 citations, lay the groundwork for this approach, proposing methods to map human cognitive processes onto robotic perception. Though early in its citation impact, this research is foundational for advancing socially aware robotics and autonomous surveillance systems. Zeni’s work is particularly notable for bridging the gap between human cognition and machine learning, offering a pathway toward more empathetic and effective human-robot collaboration in security, assistance, and beyond.