Max Robotics — Global Service Robot Directory + US Certification

About

Guobao Zhang’s research lies at the intersection of affective computing, intelligent robotics, and multi-criteria decision analysis, with a focus on enabling machines to perceive and respond to human emotional states. His early work pioneered speech emotion recognition for robot pets, introducing wavelet neural networks and stacked generalization ensemble architectures to classify affective states from speech signals. These foundational studies, each cited 5 times, demonstrated how robots could interpret human emotions using limited acoustic data—a critical step toward socially aware human-robot interaction. Zhang later advanced decision-making theory by extending prospect theory to robot evaluation and selection, accounting for risk preferences and interactive criteria under uncertainty, a contribution that bridges behavioral economics and robotics. More recently, he has tackled the practical challenge of dynamic environments in SLAM (Simultaneous Localization and Mapping), proposing hybrid frameworks for robust 3D point cloud removal and online dynamic object separation. His 2023 and 2024 works address the critical problem of map degradation caused by moving objects, particularly in low-resolution LiDAR systems. Though his citation counts remain modest, Zhang’s trajectory from emotion recognition to dynamic perception reflects a coherent vision: building robots that are not only emotionally attuned but also spatially robust in real-world, changing environments.