Papers
9
Total Citations
113
H-Index
8
About
Yunkai Li is a researcher whose work sits at the compelling intersection of human-robot interaction, affective computing, and tactile sensing. His research addresses two core challenges: enabling robots to physically sense and interpret their environments through advanced tactile technologies, and equipping intelligent systems with the ability to recognize human emotions and gestures through touch. In the domain of tactile sensing, Li pioneered the development of magnetostrictive sensor arrays using Galfenol (Fe83Ga17 alloy), leveraging smart materials to give robotic fingers sophisticated object detection and recognition capabilities — work that has accumulated nearly 40 citations across multiple studies. His later research shifted toward affective human-robot interaction, where he developed deep learning architectures employing spatiotemporal convolutions and attention mechanisms to recognize touch gestures and infer emotional states. His multimodal fusion work, including the MMFN framework combining touch and facial expression data, reflects an increasingly holistic approach to robotic perception. With over 113 cumulative citations across nine notable publications, Li's contributions span sensor hardware design through to sophisticated neural network modeling. His cross-subject domain adaptation work (MASS) further demonstrates a commitment to practical, generalizable solutions — pushing socially assistive robotics meaningfully closer to real-world deployment.
Research Focus
Key Achievements
Top Papers
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- 2Magnetostrictive Tactile Sensor Array for Object Recognition19 citations · 2019
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- 4Touch Gesture Recognition Using Spatiotemporal Fusion Features12 citations · 2021
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