Papers
8
Total Citations
76
H-Index
5
About
Chuzhao Liu is a robotics researcher whose work centers on legged locomotion, motion planning, and disturbance rejection for humanoid and quadruped robots. His major contributions include developing a multitasking-oriented robot arm motion planning scheme that integrates deep reinforcement learning with digital twin technology—a concept aligned with Industry 4.0 and Made in China 2025. Liu has also advanced bipedal robot stability through a deep reinforcement learning-based disturbance rejection control method, and tackled highly dynamic motions like vertical jumping using quadratic programming optimization to handle over-constrained control objectives. His early work on miniature reconnaissance robots explored low-cost map building and obstacle avoidance, demonstrating a commitment to practical, resource-constrained systems. With over 75 citations across his most-cited papers, Liu’s research has influenced both theoretical control frameworks and applied robotics. Notably, his 2020 paper on deep reinforcement learning for robot arms has garnered 35 citations, reflecting its impact on the field. Liu’s work bridges simulation and real-world deployment, offering students and researchers a clear example of how reinforcement learning and optimization can solve complex, real-time control challenges in robotics.
Research Focus
Key Achievements
Top Papers
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- 3Vertical Jumping for Legged Robot Based on Quadratic Programming9 citations · 2021
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- 6Stability analysis of quadruped robot based on compliant control5 citations · 2016
- 7The 3D map building of the mobile robot2 citations · 2016
- 8Navigation research on outdoor miniature reconnaissance robot2 citations · 2016