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About

Bao Xin Chen is a robotics researcher whose work focuses on enabling social robots to perceive, track, and interact with humans in dynamic indoor environments. His primary research areas include person-following robots, visual tracking, scene classification, and indoor localization. Chen’s most significant contribution is in person-following behavior for social robots, where he developed novel approaches integrating stereo vision with adaptive tracking algorithms. His 2017 paper on integrating stereo vision with a CNN tracker (66 citations) and his work on selected online Ada-Boosting with stereo cameras (47 citations) address critical challenges such as occlusion and illumination changes that cause robots to lose track of their target. Chen has also advanced scene classification for robotics by implementing context-based word embeddings to improve accuracy across increasing numbers of scene classes. His work on indoor localization tackles the difficult problem of generalizing visual odometry and global pose refinement methods from static to dynamic human environments. Through these contributions, Chen has helped lay the groundwork for more robust, socially-aware robots capable of operating alongside humans in real-world settings.