Gang Yan
Papers
6
Total Citations
101
H-Index
5
About
Gang Yan is a robotics researcher whose work sits at the intersection of tactile sensing, robotic manipulation, and multimodal machine learning. His research has made significant contributions to two of the most challenging problems in robotic grasping: predicting grasp stability before object lifting and detecting slip during manipulation. His 2022 paper on vision-touch fusion for slip detection (34 citations) demonstrated that combining visual and tactile modalities could outperform conventional tactile-only approaches, helping to advance the field of multimodal robotic learning. His SCT-CNN architecture (25 citations) introduced a novel spatio-channel-temporal attention mechanism for grasp stability prediction, while his ensemble learning framework (24 citations) unified stability prediction and slip detection into a cohesive multi-phase pipeline. Beyond grasping, Yan has explored texture recognition using deep GRU ensembles with soft skin sensors and contributed to humanoid rehabilitation robotics through skeleton-based motion generation. His most recent work extends tactile intelligence to precise insertion tasks, reflecting a growing interest in fine manipulation. With nearly 100 citations across his portfolio, Gang Yan has established himself as a productive and innovative voice in tactile robotics research.
Research Focus
Key Achievements
Top Papers
- 1Detection of Slip from Vision and Touch34 citations · 2022
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