Yuanfang Wan

Beijing University of Chemical Technology, Southern University of Science and Technology, Peking University

Papers

Total Citations

H-Index

About

Yuanfang Wan is a researcher at the forefront of bio-inspired robotics and intelligent control systems. Her work primarily spans three interconnected domains: neural signal processing for prosthetics, bio-mimetic sensing for underwater robots, and swarm robotics simulation. Wan’s most notable contribution is her pioneering application of convolutional neural networks to decode surface electromyography (sEMG) signals for bionic manipulator control—a breakthrough that directly advances intelligent neuroprostheses for amputees. This highly cited 2018 study (21 citations) demonstrates how deep learning can bridge the gap between biological signals and robotic actuation, offering a pathway toward more natural, intuitive prosthetic control. She has also developed the HeROS simulation platform for heterogeneous robotic swarms, addressing a critical need for efficient, scalable testing environments in multi-robot systems. More recently, Wan has explored artificial lateral line systems for hydrodynamic force estimation, drawing inspiration from fish to create novel underwater sensing capabilities. Her research portfolio—while still early in impact trajectory—shows a clear commitment to translating biological principles into practical robotic systems, with potential applications ranging from medical rehabilitation to autonomous underwater exploration.