Mofeed Nagib
Papers
1
Total Citations
2
H-Index
1
About
Mofeed Nagib is a rising researcher at the forefront of human-robot interaction, with a focused expertise in designing socially intelligent robots that adapt to human expectations. His most-cited work, “Predicting Human Perceptions of Robot Performance during Navigation Tasks” (2025, 2 citations), tackles a critical challenge: understanding how people perceive robot behavior without disrupting natural interactions. Rather than relying on intrusive surveys, Nagib pioneers predictive models that infer human perceptions in real time, enabling robots to adjust their navigation and social cues dynamically. This contribution bridges the gap between technical performance and human comfort, offering a scalable path toward more intuitive and acceptable robotic systems. Though early in his career, Nagib’s work has already garnered attention for its practical implications in autonomous navigation and collaborative robotics. His approach promises to reshape how robots learn from and respond to human feedback, making him a notable voice in the next generation of human-aware robotics.
Research Focus
Key Achievements
Top Papers
- 1