Xipeng Huang
Papers
1
Total Citations
4
H-Index
1
About
Xipeng Huang is a rising researcher at the intersection of human-robot interaction and generative AI, whose work reimagines how people communicate with machines. His most-cited paper, "GenComUI: Exploring Generative Visual Aids as Medium to Support Task-Oriented Human-Robot Communication" (2025, 4 citations), introduces a novel system that leverages large language models to dynamically produce contextual visual aids—such as map annotations, path indicators, and animations—bridging the gap between verbal commands and robotic understanding. This contribution addresses a critical bottleneck in collaborative robotics: making task-oriented communication more intuitive and efficient. Huang’s research centers on designing generative interfaces that enhance human-robot collaboration, with implications for manufacturing, service robotics, and assistive technologies. His work stands out for its practical focus on real-time visual feedback, reducing cognitive load for human operators. As an early-career scholar, Huang has already demonstrated the ability to tackle complex, interdisciplinary challenges, earning recognition for his innovative approach to human-robot communication. His findings offer a compelling foundation for future studies in adaptive, AI-mediated interaction systems.
Research Focus
Key Achievements
Top Papers
- 1