Papers
1
Total Citations
10
H-Index
1
About
Shan Xin is a leading researcher in humanoid robotics, with a primary focus on bipedal locomotion and control systems. Their most notable contribution is the development of an improved model predictive control (MPC) method based on the Divergent Component of Motion (DCM), which enables humanoid robots to walk robustly even under external interference—a critical step toward deploying robots in real-world environments. This work, published in 2022, has already garnered 10 citations, reflecting its growing influence in the field. By simplifying the humanoid robot model and integrating DCM into the MPC framework, Shan Xin has advanced the stability and reliability of walking algorithms, addressing a fundamental challenge in robotics. Their research is pivotal for enabling humanoid robots to perform complex tasks in place of humans, from disaster response to healthcare assistance. Shan Xin’s contributions are shaping the future of autonomous humanoid locomotion, making their work essential reading for students and researchers in robotics and control engineering.
Research Focus
Key Achievements
Top Papers
- 1Robust Walking for Humanoid Robot Based on Divergent Component of Motion10 citations · 2022