Yibing Nan

Papers

Total Citations

H-Index

About

Yibing Nan is a robotics and computer vision researcher whose work sits at the intersection of autonomous navigation and human-robot interaction. With a focus on enabling robots to perceive and respond to their environments more intelligently, Nan has made meaningful contributions to two critical challenges in modern robotics: safe autonomous movement and naturalistic human-robot communication. Nan's most recognized contribution, "Small Obstacle Avoidance Based on RGB-D Semantic Segmentation" (2019), addresses a persistent blind spot in autonomous navigation — the detection of small obstacles that conventional systems routinely miss. By leveraging RGB-D sensor data combined with semantic segmentation techniques, the system allows road robots to navigate continuously and collision-free, a practical advancement for real-world deployment. This work has garnered 31 citations, reflecting its relevance to the robotics community. Complementing this, Nan's research on human-robot conversation introduced a Seq2Seq-based gesture interaction system that enables robots to produce realistic body language during dialogue, earning 11 citations. Together, these works demonstrate Nan's commitment to building robots that are not only spatially aware but also socially intelligent — a dual focus that positions this researcher as a thoughtful contributor to the future of interactive autonomous systems.