Xiaopeng Wu
Papers
2
Total Citations
12
H-Index
2
About
Xiaopeng Wu is a pioneering researcher in the field of soft robotics and tactile sensing, with a primary focus on electrical impedance tomography (EIT)-based sensor systems. His major contributions center on developing cost-effective, scalable tactile sensors that can maintain performance even on highly deformable surfaces—a critical challenge for soft robotic applications. Wu's most cited work, "Learning-Enhanced Electronic Skin for Tactile Sensing on Deformable Surface Based on Electrical Impedance Tomography" (2025, 9 citations), introduces machine learning approaches to overcome performance degradation in EIT sensors when applied to flexible substrates. His foundational research in "Tactile sensing on deformed surfaces with electrical impedance tomography" (2024, 3 citations) demonstrates how sparse electrode configurations can enable large-area coverage while maintaining safety and affordability. Wu's innovative combination of learning algorithms with traditional EIT techniques represents a significant advancement in creating robust electronic skins for robots operating in unstructured environments. His work is particularly notable for addressing the practical limitations that have historically prevented EIT-based sensors from being deployed on highly deformable robotic surfaces, opening new possibilities for human-robot interaction and adaptive manipulation.
Research Focus
Key Achievements
Top Papers
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