Fangming Guo
Papers
1
Total Citations
5
H-Index
1
About
Fangming Guo is a leading researcher in the field of robotic tactile sensing, with a primary focus on developing advanced computational models for event-driven touch perception. Their most-cited work, "Event-Driven Tactile Sensing With Dense Spiking Graph Neural Networks" (2025, 5 citations), introduces a novel framework that leverages spiking neural networks to process high-temporal-resolution tactile data from event-driven sensors. This approach significantly enhances robots' ability to perform object recognition, manipulation, and grasping with lower energy consumption and faster response times. Guo's contributions address a critical bottleneck in robotics: enabling machines to interpret complex tactile signals in real time, much like human touch. By integrating dense graph neural networks with spiking architectures, they have advanced the efficiency and accuracy of tactile feedback systems. Their work is gaining traction among researchers in neuromorphic computing and soft robotics, positioning Guo as an emerging voice in the intersection of bio-inspired sensing and robotic dexterity. With growing citations, their research promises to unlock more intuitive and energy-efficient robotic interactions with the physical world.
Research Focus
Key Achievements
Top Papers
- 1Event-Driven Tactile Sensing With Dense Spiking Graph Neural Networks5 citations · 2025