Guoyi Fu

Papers

Total Citations

H-Index

About

Guoyi Fu is a researcher advancing the frontier of robotic manipulation through semantic 3D perception. His primary research areas include category-level robotic manipulation, 3D keypoint detection, and computer vision for service robotics. Fu’s most notable contribution is the development of SKP (Semantic 3D Keypoint Detection), a framework that enables robots to robustly manipulate objects with intra-category variations in shape, size, and appearance—a critical capability for real-world applications in food service and hospitality. This work, published in 2022, has already garnered 18 citations, reflecting its timely impact on the field. By moving beyond reliance on full 3D shape estimation, Fu’s approach allows for more efficient and generalizable robotic grasping and handling of everyday objects. His research directly addresses the challenge of service vision, where robots must interact with diverse, unconstrained items. Fu’s work stands out for its practical orientation, aiming to bridge the gap between controlled lab environments and the messy, variable settings where assistive robots are increasingly deployed.