Papers
11
Total Citations
136
H-Index
6
About
Di Fu is a researcher whose work sits at the intersection of human-robot interaction, cognitive science, and social robotics. Her research focuses on endowing robots with socially intelligent capabilities — including personality modeling, emotional expression, and crossmodal attention — to foster more natural and trustworthy human-robot relationships. One of her most influential contributions, "Do I Have a Personality? Endowing Care Robots with Context-Dependent Personality Traits" (2020, 52 citations), demonstrated how context-sensitive personality traits can significantly enhance interaction quality in care robotics. Her work on computational models of selective attention (29 citations) bridges cognitive neuroscience and artificial perception, offering frameworks applicable to both unimodal and crossmodal processing. Fu has also made notable advances in neurorobotics, training humanoid robots like iCub to resolve crossmodal social conflicts in ways that mirror human behavior. More recently, her investigations into how robot voice naturalness and emotional displays shape user trust and compliance address urgent ethical questions in an era of increasingly sophisticated text-to-speech systems. Through tools like Wrapyfi — a middleware integration framework — she further contributes to the practical infrastructure of robotics research. Collectively, her body of work advances our understanding of how robots can become safer, more intuitive, and socially aware collaborators.
Research Focus
Key Achievements
Top Papers
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- 3Influence of Robots’ Voice Naturalness on Trust and Compliance12 citations · 2024
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- 7Human Impression of Humanoid Robots Mirroring Social Cues4 citations · 2024
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