Papers
10
Total Citations
73
H-Index
4
About
Senlin Fang is a robotics and machine learning researcher whose work sits at the intersection of tactile sensing, deep learning, and robotic manipulation. His research focuses on developing advanced computational methods that enable robots to perceive and interpret touch-based information with greater accuracy and efficiency — a capability fundamental to autonomous robotic systems operating in dynamic, real-world environments. Fang's most-cited contributions include TactONet (2022, 23 citations), a novel tactile ordinal network leveraging unimodal probability distributions to improve object hardness classification, and a graph convolutional network framework for grasp stability prediction (2021, 20 citations), which significantly advances robots' ability to assess whether grasps will succeed before object slippage occurs. His broader portfolio spans multi-label tactile adjective recognition, attention-based fusion networks for grasp outcome prediction, and pioneering applications of spiking neural networks (SNNs) to tactile continual learning — addressing the critical challenge of catastrophic forgetting as robots encounter new tasks over time. Particularly notable is Fang's exploration of event-based tactile sensors paired with sparse probabilistic SNNs, reflecting a forward-looking approach to energy-efficient robotic perception. Across ten publications accumulating over 70 citations, his work consistently pushes the boundary of how machines sense, learn from, and act upon touch — making him an emerging voice in intelligent robotic perception research.
Research Focus
Key Achievements
Top Papers
- 1
- 2Tactile Grasp Stability Classification Based on Graph Convolutional Networks20 citations · 2021
- 3Leveraging Multi-label Correlation for Tactile Adjective Recognition6 citations · 2020
- 4
- 5
- 6
- 7
- 8
- 9
- 10