Papers
8
Total Citations
479
H-Index
6
About
Lap-Fai Yu is a leading researcher at the intersection of computer vision, robotics, and human-robot interaction. His work spans scene understanding, assistive robotics, and affordance reasoning, with a particular focus on creating intelligent systems that can perceive, navigate, and interact with complex environments. Yu’s most impactful contribution is the SceneNN dataset (346 citations), a richly annotated RGB-D scene dataset that has become a foundational resource for 3D scene understanding and robotics research. He has also pioneered assistive technology for the blind and visually impaired, developing a deep learning-based robotic guide dog that learns trail-following behaviors from both virtual and real-world environments (73 citations). His innovative work on liquid containability reasoning introduced a physics-based approach to affordance prediction, enabling robots to understand object functionality beyond simple geometry. More recently, Yu has advanced human-robot collaboration through federated learning for grasping in handover tasks and designed frameworks for human-robot teaming in navigation among movable obstacles. His research on human-robot coexistence spaces and mixed-reality worker-drone interaction demonstrates a commitment to safe, effective human-robot collaboration in real-world settings.
Research Focus
Key Achievements
Top Papers
- 1SceneNN: A Scene Meshes Dataset with aNNotations346 citations · 2016
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- 4Fed-HANet: Federated Visual Grasping Learning for Human Robot Handovers11 citations · 2023
- 5Designing Human-Robot Coexistence Space9 citations · 2021
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