Pauline Trung
Papers
1
Total Citations
32
H-Index
1
About
Pauline Trung is a leading researcher in human-robot interaction (HRI), with a specific focus on the subtle, non-verbal cues that reveal when a robot has made a mistake. Her key research areas include automatic error detection, social signal processing, and the design of more intuitive, responsive robotic systems. Trung’s most significant contribution is her pioneering work on using human head and shoulder movements as a reliable, real-time indicator of robot errors. Her highly cited 2017 paper, "Head and shoulders: automatic error detection in human-robot interaction" (32 citations), introduced a novel classification method that allows robots to "see" when they have confused or frustrated a human partner, enabling them to adapt and correct their behavior without explicit verbal feedback. This work has profound implications for creating safer, more collaborative robots in settings from manufacturing to home assistance. By shifting the focus from what the robot does to how the human reacts, Trung has opened a new pathway for building machines that are not just functional, but genuinely perceptive partners.
Research Focus
Key Achievements
Top Papers
- 1Head and shoulders: automatic error detection in human-robot interaction32 citations · 2017