Hui-Rang Hou
Papers
7
Total Citations
85
H-Index
5
About
Hui-Rang Hou is a leading researcher at the intersection of human-robot interaction, affective computing, and autonomous navigation. Their work focuses on enabling robots to perceive and respond to human emotional states through touch, pioneering the use of decomposed spatiotemporal convolutions for touch gesture and emotion recognition. Hou’s most cited paper (23 citations) establishes a foundation for social robots to interpret affective touch, while their MMFN framework (15 citations) advances the field by fusing touch gesture with facial expression data for more robust emotion recognition. Addressing the critical challenge of individual variability, Hou developed MASS, a multisource domain adaptation network for cross-subject touch gesture recognition (8 citations). Beyond social interaction, Hou has made significant contributions to robot olfaction and safe navigation, including a gradient adaptive extremum seeking search for odor source localization (22 citations) and Safe-Nav, a learning-based approach to prevent navigation failures in unknown environments (8 citations). Their work on touch-text answer generation via supervised adversarial learning (5 citations) further bridges tactile sensing with natural language, demonstrating a comprehensive approach to creating more intuitive and capable robotic systems.
Research Focus
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Top Papers
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- 7A Deep Learning-Based Indoor Odor Compass4 citations · 2023