Meiying Li
Papers
1
Total Citations
4
H-Index
1
About
Meiying Li is a rising scholar at the intersection of human-robot interaction and generative AI, with a core focus on designing intuitive communication systems for collaborative robotics. Her most prominent work, "GenComUI: Exploring Generative Visual Aids as Medium to Support Task-Oriented Human-Robot Communication" (2025), introduces a novel framework that leverages large language models to dynamically produce contextual visual aids—such as map annotations, path indicators, and animations—during verbal task exchanges. This contribution addresses a critical gap in human-robot collaboration: enabling robots to generate real-time, adaptive visual feedback that reduces ambiguity and enhances task efficiency. Though early in her career, Li’s research has already garnered attention, with her flagship paper accumulating 4 citations within its first year, signaling growing interest from the HRI and AI communities. Her work stands out for its practical integration of generative models into embodied systems, offering a scalable pathway toward more natural and effective human-robot teamwork. As she continues to explore multimodal communication and user-centered design, Meiying Li is poised to shape the future of how humans and machines collaborate in complex, real-world environments.
Research Focus
Key Achievements
Top Papers
- 1