Papers
6
Total Citations
19
H-Index
3
About
Yupo Zhang is a leading researcher at the intersection of robotics, tactile perception, and neuromorphic computing, with a focus on enabling robots to interact with the physical world through a refined sense of touch. Their work addresses critical challenges in robotic manipulation, particularly for high-precision industrial tasks like snap-fit assembly in electronics manufacturing. Zhang’s major contributions include pioneering the use of event-based optical sensors (Evetac) with sparse probabilistic spiking neural networks to dramatically improve the speed and accuracy of tactile recognition. They have also advanced continual learning in robotics through TactCLNet, a generative replay framework that allows robots to learn new tactile tasks without forgetting previous ones, and developed TempTrans-MIL, a general method for processing complex multimodal tactile time-series data. With over 19 citations across six recent publications (2023-2025), Zhang’s research is gaining rapid traction. Their innovative self-supervised contrastive learning approach for grasp outcome prediction further demonstrates a commitment to reducing the need for labeled data. Zhang is at the forefront of making robotic touch more adaptive, efficient, and intelligent for real-world applications.
Research Focus
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Top Papers
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