Ahnjae Shin
Papers
1
Total Citations
5
H-Index
1
About
Ahnjae Shin is a researcher in human-robot interaction (HRI), with a focus on designing intuitive, human-in-the-loop systems that enable more natural and less cognitively demanding robot control. Her most cited work, "Apprentice of Oz: Human in the Loop System for Conversational Robot Wizard of Oz" (2019), addresses a core challenge in HRI: the high cognitive load placed on human operators when using the Wizard of Oz protocol to control conversational robots. Shin proposed a system that learns from operator demonstrations, reducing the burden of simultaneously managing a robot’s movement and speech. This contribution is foundational for creating more fluid, semi-autonomous robot control interfaces, with the paper accumulating 5 citations. While still early in her career, Shin’s work is notable for its practical approach to improving operator efficiency and realism in HRI studies, laying groundwork for future systems that blend human guidance with machine learning. Her research sits at the intersection of robotics, interaction design, and cognitive ergonomics, promising to make robot teleoperation more accessible for both researchers and practitioners.
Research Focus
Key Achievements
Top Papers
- 1