Hong Qiao
Chinese Academy of Sciences, University of Science and Technology of China, Shandong Institute of Automation, Institute of Automation, University of Chinese Academy of Sciences, De Montfort University, Center for Excellence in Brain Science and Intelligence Technology, City University of Hong Kong, University of Manchester, University of Tehran
Papers
102
Total Citations
2,479
H-Index
28
About
Hong Qiao is a prominent robotics researcher whose work spans high-precision robotic manipulation, brain-inspired intelligent robotics, and adaptive control systems. Based at the Institute of Automation, Chinese Academy of Sciences, Qiao has made transformative contributions to both industrial and service robotics, addressing some of the field's most challenging problems with remarkable ingenuity. Qiao is perhaps best known for pioneering the concept of the "Attractive Region in Environment" (2015, 142 citations), a groundbreaking framework enabling high-precision manipulation without reliance on high-precision sensors — a paradigm shift for manufacturing robotics. Her widely cited survey on robotic grasping and assembly strategies (2019, 173 citations) has become an essential reference for researchers navigating this rapidly evolving domain. Equally influential is her work on adaptive neural network control for wheeled mobile robots (2020, 160 citations), demonstrating her versatility across control theory and learning-based approaches. Qiao has also championed brain-inspired robotics, producing influential surveys and theoretical frameworks (2021, 108 citations; 2023, 76 citations) that envision robots integrating vision, decision-making, and musculoskeletal systems inspired by human neuroscience. Her research on muscle-synergy-based neuromuscular control further bridges biological principles with next-generation robotic design. With a body of work accumulating hundreds of citations across multiple disciplines, Qiao stands as a leading voice shaping the future of intelligent robotics.
Research Focus
Key Achievements
Top Papers
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- 8Improving performance of robots using human-inspired approaches: a survey73 citations · 2022
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