Feihu Song

Intelligent Health (United Kingdom), South China University of Technology

Papers

Total Citations

H-Index

About

Feihu Song is a rising leader in bioinspired soft robotics and intelligent actuation, whose work bridges the gap between biological mimicry and high-performance engineering. His research centers on developing novel artificial muscles and soft robotic systems that integrate multimodal sensing, shape locking, and energy efficiency. Song’s most cited work, “Bionic Muscle with Dual-Mode Sensing Function Inspired by Plant Tendrils” (2025, 8 citations), introduces a groundbreaking bioinspired actuator that mimics the sensory and motile properties of plant tendrils, enabling simultaneous actuation and environmental perception—a long-standing challenge in the field. He further advanced the field with “Programmable Helical Hierarchy in Coiled Polymer Artificial Muscles” (2025, 4 citations), which solves the trade-off between stroke, payload, and structural programmability through a multilevel helical fabrication method. His 2024 paper on an energy-efficient soft robotic gripper with shape locking and sensing (1 citation) demonstrates practical applications in sustainable robotics. Song’s work is notable for its elegant integration of biological principles with scalable fabrication techniques, positioning him as a key innovator in next-generation soft robotics.