Papers
5
Total Citations
176
H-Index
4
About
Xinghua Wu is a leading researcher in tactile sensing and terrain-adaptive locomotion for small legged robots, with a focus on integrating sensor design, machine learning, and control systems. Her major contributions include the development of thin, robust capacitive tactile sensors that measure ground reaction forces, enabling real-time terrain classification using support vector machines—work that has garnered over 70 citations per paper. She also pioneered tactile sensing for gecko-inspired dry adhesives, advancing climbing and grasping capabilities in robotics. Wu’s research extends to contact event detection in robotic oil drilling, where acoustic sensing ensures safe pipe manipulation in noisy environments, and multi-stage visual servoing for autonomous assembly. Her work has been cited over 170 times, reflecting its impact on robotics and automation. Notably, her 2019 paper on tactile sensing and gait control for small legged robots stands out for its practical sensor design and dynamic control integration, while her 2016 study on terrain classification remains a foundational reference in the field. Wu’s innovative approach to sensor-driven robotics continues to inspire advances in autonomous systems and human-robot interaction.
Research Focus
Key Achievements
Top Papers
- 1Tactile Sensing and Terrain-Based Gait Control for Small Legged Robots74 citations · 2019
- 2
- 3Tactile sensing for gecko-inspired adhesion20 citations · 2015
- 4Contact event detection for robotic oil drilling7 citations · 2014
- 5