Shangchun Liao

Wuhan University of Science and Technology

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About

Shangchun Liao is a robotics researcher whose work bridges human–machine interaction and intelligent manipulation systems. His primary research areas include surface electromyography (sEMG)-based gesture recognition, multi-robot coordination, and climbing robot structural design. Liao’s most influential contribution is his 2020 study on multi-object intergroup gesture recognition, which integrates fusion feature extraction with a K-nearest neighbor (KNN) algorithm to decode sEMG signals from activated muscle regions. This work, cited 65 times, advances rehabilitation robotics by enabling more intuitive, muscle-driven control interfaces. In 2023, Liao extended his focus to dual-manipulator systems, proposing a Markov decision process framework combined with neural networks for grasping detection—a step toward autonomous, adaptive robotic hands. His earlier analysis of wall-climbing robot structural performance (2019) further demonstrates his versatility in addressing real-world robotic challenges, from mobility to stability. By combining signal processing, machine learning, and mechanical analysis, Liao’s research contributes to making robots more responsive and practical in assistive and industrial settings.