Binhua Huang
Papers
5
Total Citations
54
H-Index
3
About
Binhua Huang is a rising researcher at the intersection of robotics, tactile sensing, and intelligent perception. His work centers on enabling robots to interact with and understand their physical environment, with key contributions in robotic palpation for medical diagnosis, multi-sensor human motion tracking, and dexterous robotic grasping. Huang’s most cited paper, “Methods to Recognize Depth of Hard Inclusions in Soft Tissue Using Ordinal Classification for Robotic Palpation” (2022, 27 citations), addresses a critical challenge in tumor resection by developing a classification method to determine tumor depth during robotic palpation. He has also advanced human motion tracking with a Shortcut Enhanced LSTM-GCN network (2023, 17 citations), improving multi-sensor tracking without expensive optical equipment. His work on a spring-based rigid-soft gripper for conformal grasping and object recognition (2024), dynamic liquid volume estimation using optical tactile sensors and spiking neural networks (2023), and self-supervised contrastive learning for grasp outcome prediction (2023) demonstrates a commitment to integrating learning and sensing for more capable, autonomous robotic systems. With a growing citation record and a focus on practical, clinically relevant applications, Huang is establishing himself as an innovator in soft robotics and intelligent manipulation.
Research Focus
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Top Papers
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