Yusen Wan
Papers
2
Total Citations
9
H-Index
2
About
Yusen Wan is a rising researcher at the forefront of robotic manipulation and tactile sensing, with a focused expertise in slip detection and contact force estimation. His work addresses a critical gap in dexterous robotics: enabling machines to detect and prevent object slippage during grasping with a sensitivity approaching human touch. Wan’s major contribution lies in developing a learning-based framework that estimates the contact force field from tactile sensor data and leverages entropy properties to robustly identify slip events—a novel approach that moves beyond traditional visual feedback. His most-cited paper, “Learning to Detect Slip Through Tactile Estimation of the Contact Force Field and its Entropy Properties” (2024), has already garnered 7 citations, signaling strong early impact in the tactile sensing community. By integrating artificial tactile perception with machine learning, Wan is helping to close the performance gap between robotic and human manipulation, paving the way for more reliable and autonomous object handling in manufacturing, prosthetics, and service robotics. His work represents a promising step toward truly dexterous robotic hands.
Research Focus
Key Achievements
Top Papers
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- 2