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About

Hung-Jui Huang is a robotics and computer vision researcher whose work spans visual place recognition, robotic manipulation, and multimodal perception. He is perhaps best known for developing the Omnidirectional Convolutional Neural Network (O-CNN), a novel deep learning architecture designed to handle severe camera pose variation in visual place recognition tasks using omnidirectional cameras. This foundational contribution has garnered over 87 citations across multiple venues, establishing Huang as a meaningful voice in robot navigation and scene understanding. Beyond navigation, Huang's research demonstrates a compelling interest in enabling robots to perceive and interact with complex real-world environments. His work on intelligent cooking robots tackles the non-trivial challenge of perceiving and manipulating liquids with varying properties, culminating in a system capable of autonomously mixing batter and producing pancakes. More recently, his FusionSense framework pushes the frontier of 3D reconstruction by bridging foundation model priors with sparse visual and tactile observations — mirroring how humans integrate common sense with sensory experience. Together, these contributions reflect a research vision centered on building robots that perceive richly, reason broadly, and act skillfully across diverse and unstructured environments.