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About

Chaoxiang Ye is a robotics researcher advancing tactile perception and multi-sensor fusion for intelligent manipulation. His work centers on three interconnected areas: tactile object recognition, grasp stability analysis, and human motion tracking. Ye’s major contributions include developing TactONet, a tactile ordinal network that leverages unimodal probability distributions to classify object hardness—a critical property for robotic grasping—achieving 23 citations since 2022. He also pioneered the use of graph convolutional networks for tactile grasp stability classification, enabling robots to predict object slippage at the onset of grasping, a method cited 20 times. In human motion tracking, Ye introduced a Shortcut Enhanced LSTM-GCN network that adapts to changing joint positions, reducing reliance on expensive optical motion capture (17 citations). His most recent work explores dynamic liquid volume estimation using optical tactile sensors paired with spiking neural networks, pushing toward energy-efficient, event-driven perception. With a total of 63 citations across his top papers, Ye’s research directly addresses real-world robotic challenges—from handling deformable objects to stable grasping—making his work essential reading for students and researchers in tactile robotics and sensorimotor control.