Papers
30
Total Citations
664
H-Index
15
About
Guiyang Xin is a robotics researcher whose work spans legged locomotion, motion control, and human-robot interaction, with a particular focus on developing robust, adaptive controllers for quadruped and hexapod robots. His research bridges model-based and learning-based approaches, making significant contributions to both the theoretical foundations and practical deployment of legged systems in challenging real-world environments. Xin's early work on hexapod robots established dynamic hybrid control frameworks and disaster-rescue locomotion strategies, garnering over 50 citations each. He later pioneered impedance control methodologies for legged systems, developing principled approaches for online optimal impedance planning and stiffness-damping selection that remain widely referenced, with multiple papers exceeding 35 citations. His optimization-based locomotion controller leveraging Cartesian impedance control and his LQR-enhanced footstep planning framework further solidified his reputation in robust quadrupedal locomotion. Most recently, Xin co-developed a blind locomotion system integrating Adversarial Motion Priors, enabling legged robots to traverse demanding terrains both robustly and agilely — his most cited work with 90 citations since 2023. Across his portfolio, Xin's research consistently addresses the challenge of making legged robots safer, more compliant, and more capable, positioning him as an influential voice in next-generation autonomous robotic locomotion.
Research Focus
Key Achievements
Top Papers
- 1Learning Robust and Agile Legged Locomotion Using Adversarial Motion Priors90 citations · 2023
- 2
- 3Dynamic Hybrid Control of a Hexapod Walking Robot: Experimental Verification51 citations · 2016
- 4
- 5Robust Footstep Planning and LQR Control for Dynamic Quadrupedal Locomotion39 citations · 2021
- 6Choosing Stiffness and Damping for Optimal Impedance Planning37 citations · 2022
- 7
- 8Online Optimal Impedance Planning for Legged Robots35 citations · 2019
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- 10