Papers
8
Total Citations
138
H-Index
6
About
Bugong Xu is a prominent robotics researcher whose work spans human-robot interaction, teleoperation, motion optimization, and intelligent control systems. Based at a leading Chinese institution, Xu has made substantial contributions to advancing robotic manipulation and autonomous navigation over two decades of research. Xu's most celebrated work focuses on skill learning for human-robot cooperative manipulation, where his 2020 paper — garnering 54 citations — introduced a hierarchical control framework that leverages dynamic motion primitives to transfer human motor skills to robotic systems, a breakthrough with significant implications for industrial and service robotics. Complementing this, his survey on bioinspired embodiment for fine manipulation (25 citations) synthesizes cutting-edge approaches to achieving human-level dexterity in robots. Earlier in his career, Xu tackled the fundamental challenge of network-induced time delays in internet-based teleoperation, developing Smith predictor-based compensation strategies that improved stability and real-time control reliability — work that established a foundation for modern remote robotic operation. His later contributions expanded into intelligent navigation, employing interval type-2 fuzzy neural networks combined with Q-learning for robust mobile robot performance in complex environments. With over 130 cumulative citations, Xu's body of work reflects a consistent dedication to bridging intelligent learning systems with practical robotic applications.
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