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About

Keli Hu is a pioneering researcher at the intersection of robotics, artificial intelligence, and computational aesthetics. Her primary research focuses on developing brain-like intelligent systems that enable robots to autonomously evaluate and improve the aesthetic quality of their own dance poses—a capability inspired by human dancers who use mirror observation for self-correction. Hu’s major contributions include creating a computable cognitive model of visual aesthetics, which serves as a novel framework for robotic self-assessment. Her most-cited work, “Feature fusion based automatic aesthetics evaluation of robotic dance poses” (2018, 14 citations), established foundational methods for integrating multiple visual features into aesthetic judgment. This was followed by her influential 2019 paper (12 citations) that formalized a cognitive mechanism for robotic aesthetic cognition, effectively mimicking human mirror-neuron systems. Her later work on hierarchical processing networks (2022, 4 citations) further refined these evaluations. With a cumulative impact of over 30 citations, Hu’s research is notable for bridging cognitive science and robotics, offering a unique pathway toward more autonomous, aesthetically-aware machines. Her work holds significant promise for human-robot interaction and creative robotics.