Jamin Goo
Papers
1
Total Citations
34
H-Index
1
About
Jamin Goo is a researcher whose work sits at the intersection of human-robot interaction and cognitive science, with a primary focus on how robots can better interpret and respond to human navigational cues. In his most-cited paper, "Using Human-Inspired Signals to Disambiguate Navigational Intentions" (2020, 34 citations), Goo introduces a novel framework that leverages subtle, human-like signals—such as gaze direction and body orientation—to help robots resolve ambiguity in human movement. This contribution is significant because it moves beyond traditional path-planning algorithms, enabling robots to infer intent rather than merely react to position. By mimicking how humans naturally communicate direction, Goo’s work has implications for safer, more intuitive autonomous systems in crowded environments like hospitals or airports. Though his citation count is still building, the paper’s interdisciplinary appeal has already sparked interest in robotics, psychology, and AI ethics. Goo’s research stands out for its human-centered approach, offering a practical bridge between machine efficiency and social intelligence—a key step toward robots that truly understand us.
Research Focus
Key Achievements
Top Papers
- 1Using Human-Inspired Signals to Disambiguate Navigational Intentions34 citations · 2020