Zhan Wang

University of Michigan–Ann Arbor, State Key Laboratory of Industrial Control Technology

Papers

Total Citations

H-Index

About

Zhan Wang is a versatile researcher whose work spans computer vision, semantic understanding, and robotics control systems. His most influential contribution lies in the domain of visual affordance recognition, where his 2015 paper "Mining Semantic Affordances of Visual Object Categories" has garnered 65 citations, establishing foundational groundwork for understanding how objects communicate their potential uses and interactions. This research is particularly significant for advancing human activity recognition in visual data and enabling more intuitive human-robot interaction, bridging the gap between perception and action in intelligent systems. Wang's expertise extends into robotics engineering, as demonstrated by his 2021 work on fuzzy PID control for spherical robots. This paper, which has accumulated 24 citations, addresses the challenging problem of attitude control in a mechanically complex spherical rolling robot using a novel yaw angle prediction approach — a practical contribution to autonomous mobile robotics. Together, these works reveal a researcher who operates at the intersection of machine perception and physical robotics, contributing both to how machines interpret visual scenes and how they physically navigate and interact with the world. His research holds clear relevance for students and practitioners working in AI-driven robotics and computer vision applications.