Kefan Yang

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About

Kefan Yang is a researcher in human-robot interaction and multimodal perception, with a focus on indoor navigation systems. Their most cited work, "SFU-store-nav: A multimodal dataset for indoor human navigation" (2020), has garnered 5 citations and represents a foundational contribution to the field. This dataset, collected through experiments involving human participants and a robot in Simon Fraser University's robotics lab, captures common gestures and movement patterns that are critical for developing intuitive human-robot communication. By providing a rich, multimodal resource, Yang has enabled other researchers to train and evaluate navigation algorithms that rely on natural human cues, bridging the gap between autonomous systems and real-world interaction. This work is particularly notable for its emphasis on ecological validity—using a realistic lab environment to gather data that reflects genuine human behavior. Yang's contributions support advances in assistive robotics, smart environments, and autonomous navigation, offering a valuable tool for researchers seeking to create robots that can seamlessly collaborate with people in indoor spaces.