Leonie Dyck
Papers
1
Total Citations
3
H-Index
1
About
Leonie Dyck is a rising researcher at the intersection of human-robot interaction and augmented reality, dedicated to making autonomous systems more trustworthy and understandable. Her work centers on the critical challenge of technical transparency—specifically, how robots can communicate their decision-making processes to human users in real time. In her most-cited paper, "Technical Transparency for Robot Navigation Through AR Visualizations" (2023), Dyck proposes that by overlaying a robot’s planned paths, sensor data, and reasoning directly into a user’s field of view via AR, we can bridge the gap between machine logic and human intuition. This foundational work, already garnering 3 citations, addresses the core adage that trust stems from understanding. Dyck’s contributions are particularly notable for their practical, user-centered approach: she doesn’t just theorize about explainable AI but builds tangible interfaces that allow non-experts to “see” why a robot moves or stops. Her research promises to accelerate the safe integration of robots into homes and public spaces, making her a key voice in the growing field of transparent autonomy.
Research Focus
Key Achievements
Top Papers
- 1Technical Transparency for Robot Navigation Through AR Visualizations3 citations · 2023