Ming Zhu

Hunan University, Chinese Academy of Sciences

Papers

Total Citations

H-Index

About

Ming Zhu is a pioneering researcher at the intersection of human-robot interaction and autonomous perception systems. Her work spans two critical domains: understanding how humans perceive social robots and advancing multi-sensor fusion for autonomous driving. In her influential 2020 study on social robot proactive behavior (50 citations), Zhu revealed how robot initiative-taking shapes human perception of anthropomorphic attributes, providing foundational insights for designing more intuitive service robots in public and domestic settings. Zhu’s technical contributions are equally impactful. She developed MCF3D (22 citations), a multi-stage complementary fusion network that integrates LiDAR point clouds with RGB images for robust 3D object detection in autonomous driving and robot navigation. Building on this, she introduced PSNet (19 citations), a parallel subnetworks architecture that achieves precise LiDAR-camera registration in complex, dynamic environments. Her work addresses the fundamental challenge of sensor fusion—a critical bottleneck in real-world autonomous systems. With over 90 citations across her key publications, Zhu is recognized for bridging social robotics and autonomous perception, offering both theoretical frameworks and practical solutions that advance how robots perceive humans and their environment.