Ruiyao Liu
Papers
1
Total Citations
6
H-Index
1
About
Ruiyao Liu is a rising researcher in robotic tactile sensing, with a focus on data-efficient methods for soft, scalable touch perception. Their work centers on electrical impedance tomography (EIT)-inspired tactile sensors, which offer a cost-effective, safe, and scalable alternative to traditional tactile arrays. Liu’s major contribution is a novel data augmentation strategy for learning-based tactile reconstruction, enabling high-quality sensing with fewer physical samples—a critical advance for real-world robotics. Their most-cited paper, "Data-Efficient Tactile Sensing With Electrical Impedance Tomography" (2025), has already garnered 6 citations, signaling early impact in a rapidly evolving field. By reducing the data burden for training neural networks on sparse electrode configurations, Liu’s work bridges the gap between theoretical EIT models and practical robotic applications. This achievement not only enhances tactile feedback for manipulation tasks but also lowers barriers to deploying tactile sensors in low-cost, large-area robotic skins. Liu’s research is paving the way for more intuitive and responsive human-robot interaction, making them a promising voice in the next generation of soft robotics and intelligent sensing.
Research Focus
Key Achievements
Top Papers
- 1Data-Efficient Tactile Sensing With Electrical Impedance Tomography6 citations · 2025