Nathan Alan Moy
Papers
1
Total Citations
7
H-Index
1
About
Nathan Alan Moy is a leading researcher at the intersection of human-robot interaction and autonomous navigation, with a primary focus on enabling seamless collaboration between humans and robots in complex, unstructured environments. His most cited work, "An Evaluation Framework of Human-Robot Teaming for Navigation Among Movable Obstacles via Virtual Reality-Based Interactions" (2024, 7 citations), addresses a critical gap exposed by the DARPA Subterranean Challenge: despite advances in robot autonomy, human oversight remains essential in high-stakes scenarios. Moy’s framework leverages virtual reality to systematically evaluate how humans and robots can effectively team up to navigate cluttered, dynamic spaces—such as disaster zones or subterranean tunnels—where obstacles must be moved or circumvented. This contribution is pivotal for developing more intuitive interfaces and trust-building protocols in human-robot teams. By quantifying performance metrics like task efficiency and cognitive load, Moy’s work directly informs the design of safer, more reliable robotic systems for hazardous missions. His research not only advances theoretical models of shared autonomy but also provides practical tools for engineers and operators, making him a key figure in the next generation of human-robot collaboration.
Research Focus
Key Achievements
Top Papers
- 1