Papers
3
Total Citations
22
H-Index
2
About
Wendong Zheng is a leading researcher in intelligent robotic perception, specializing in tactile sensing and cross-modal material recognition. His work bridges machine learning and sensor technology to enhance robots’ ability to interact safely with dynamic environments. Zheng’s most cited paper, “Cross-modal learning for material perception using deep extreme learning machine” (2019, 13 citations), introduced a novel framework that fuses visual and tactile data for robust material classification, advancing autonomous object handling. He further pioneered adaptive electrical resistance tomography (ERT) for large-area tactile sensing, as detailed in his 2023 paper (7 citations), which enables robots to perceive physical contact across extensive surfaces—critical for unstructured settings. His follow-up work (2024, 2 citations) refines data-driven ERT, improving real-time feedback. Zheng’s contributions are notable for their practical impact: his tactile systems allow robots to detect contact location and force without bulky sensors, enhancing safety in human-robot collaboration. By integrating deep learning with tomographic imaging, he has opened new pathways for scalable, cost-effective robotic skin. With a growing citation record, Zheng’s research is shaping the future of intelligent, perceptive robotics.
Research Focus
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