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About

Qiubo Zhong is a robotics researcher whose work spans humanoid robot motion planning, human-robot interaction, and multi-robot coordination systems. With a career built on bridging theoretical robotics and practical implementation, Zhong has made sustained contributions to some of the most technically demanding challenges in autonomous and collaborative robotics. Zhong's most recognized work focuses on humanoid robot locomotion, particularly trajectory planning for bipedal movement across uneven and complex terrain. Employing hybrid evolutionary algorithms, neural networks, and second-order cone programming, his research has advanced methods for generating stable, energy-efficient gaits — work that has collectively attracted dozens of citations. His 2016 paper on biped trajectory planning remains his most cited contribution, with 26 citations, reflecting its practical relevance to real-world deployment challenges. Beyond locomotion, Zhong has made notable strides in human-robot collaboration, including a well-cited 2015 study on Hidden Markov Model-based hand gesture recognition for industrial assembly environments, demonstrating his commitment to worker safety and intuitive human-machine interfaces. His research into multi-robot task allocation introduces novel emotional and willingness-based models, offering fresh perspectives on cooperative autonomy. Across more than a decade of publication, Zhong's interdisciplinary approach continues to enrich both the theoretical foundations and applied frontiers of intelligent robotics.