Papers
5
Total Citations
295
H-Index
4
About
Liang-Yan Gui is a leading researcher at the intersection of embodied AI, human-robot interaction, and cyber-physical systems. His work focuses on enabling robots to understand, predict, and collaborate with humans in dynamic environments. Gui’s most influential contributions include pioneering the use of meta-learning for few-shot human motion prediction—a foundational step for fluid human-robot collaboration—garnering over 125 citations. He has also advanced the safety and reliability of autonomous systems through his work on the High-Assurance SPIRAL framework, which provides end-to-end formal guarantees for robot and car control. More recently, Gui has tackled the critical challenge of situational awareness in 3D vision-language reasoning, a key bottleneck for household robots and embodied AI. His research on multi-teacher progressive distillation further addresses the practical need for lightweight, efficient vision models deployable on resource-constrained robotic platforms. With a growing citation impact exceeding 295, Gui’s work is shaping a future where robots are not only perceptive and predictive but also provably safe and context-aware.
Research Focus
Key Achievements
Top Papers
- 1Few-Shot Human Motion Prediction via Meta-learning125 citations · 2018
- 2Teaching Robots to Predict Human Motion125 citations · 2018
- 3High-Assurance SPIRAL: End-to-End Guarantees for Robot and Car Control31 citations · 2017
- 4Situational Awareness Matters in 3D Vision Language Reasoning11 citations · 2024
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