Papers
46
Total Citations
549
H-Index
14
About
Qiuguo Zhu is a robotics researcher whose work spans humanoid robot control, legged locomotion, manipulation, and autonomous navigation. His research has made significant contributions across multiple frontiers of robotics, blending classical control theory with modern machine learning to tackle complex real-world challenges. Zhu's early work focused on humanoid robots, producing influential studies on balance control, table tennis playing, and compliance strategies for maintaining stability under external disturbances — research that collectively demonstrates his deep expertise in dynamic motion generation and force control. His 2011 and 2012 papers on humanoid table tennis remain landmark demonstrations of dexterous human-robot interaction. He subsequently advanced legged robotics through adaptive torque control with series elastic actuators and terrain-aware walking algorithms for uneven surfaces. More recently, Zhu has embraced learning-based approaches, contributing the multi-expert learning architecture for adaptive locomotion, the PIE framework enabling legged robot parkour, and optimization-driven peg-in-hole assembly strategies. His 2023 RING++ paper, already accumulating 72 citations, addresses a fundamental challenge in autonomous navigation by achieving robust LiDAR-based global localization under large viewpoint variations. Together, his body of work — spanning over 300 total citations — reflects a versatile, systems-oriented approach to building capable, adaptive robotic systems.
Research Focus
Key Achievements
Top Papers
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- 3Balance motion generation for a humanoid robot playing table tennis32 citations · 2011
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- 6PIE: Parkour With Implicit-Explicit Learning Framework for Legged Robots25 citations · 2024
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- 9Regrasp Planning Using Stable Object Poses Supported by Complex Structures20 citations · 2018
- 10Impedance Control and its Effects on a Humanoid Robot Playing Table Tennis19 citations · 2012