Xiaojian Wei
Papers
1
Total Citations
6
H-Index
1
About
Xiaojian Wei is a robotics researcher whose work focuses on enabling legged robots to navigate complex, unstructured environments with greater autonomy and stability. His primary research areas include model predictive control (MPC), quadruped robot locomotion, and adaptive terrain navigation. Wei’s most notable contribution is the development of the Slope-Adaptability Model Predictive Control (SAMPC) algorithm, which allows quadruped robots to maintain stable, adaptive walking on unknown sloped terrain without relying on external vision sensors. This work, published in 2023 and garnering 6 citations, addresses a critical challenge in field robotics: how robots can sense and respond to terrain orientation using only proprioceptive feedback. By integrating slope estimation directly into the control loop, Wei’s approach enhances a robot’s ability to traverse undulating wild environments—a key step toward practical deployment in search-and-rescue, exploration, and agricultural applications. His research bridges the gap between theoretical control methods and real-world robotic performance, demonstrating how intelligent algorithms can compensate for limited sensory input. Wei’s contributions are particularly valuable for students and researchers interested in locomotion control, as they offer a clear example of how model-based methods can solve complex, real-world problems without heavy reliance on external sensors.
Research Focus
Key Achievements
Top Papers
- 1