Papers
4
Total Citations
18
H-Index
3
About
Weitao Xu is a pioneering researcher at the intersection of robotics, human-machine interaction, and distributed learning systems. His work centers on developing intelligent robotic systems capable of autonomous decision-making in complex environments, with a particular focus on multi-modal sensing and communication-efficient distributed learning. Xu’s most notable contribution is his groundbreaking work on multi-modal autonomous ultrasound scanning, where he integrates visual and tactile information into robotic systems to achieve efficient human-machine fusion interaction—a paper that has already garnered 10 citations since its 2024 publication. He has also made significant advances in distributed learning for multi-robot collaboration, introducing adaptive Top-K gradient compression in SGD to dramatically reduce communication overhead while maintaining model accuracy. His research extends to robotic arm impedance control, where he developed an enhanced neural network RBF-PID-PSO controller that addresses critical precision issues in electrical transformer calibration systems. Additionally, Xu has explored innovative contactless RF-based gait authentication for mobile devices, uncovering novel patterns in user identification. With a growing citation impact and a portfolio of work spanning from theoretical foundations to practical robotic applications, Xu is establishing himself as a rising leader in intelligent autonomous systems.
Research Focus
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Top Papers
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