Xuelian Cheng
Papers
2
Total Citations
6
H-Index
2
About
Xuelian Cheng is a pioneering researcher at the intersection of robotics, reinforcement learning, and vision-language-action (VLA) models. Their work focuses on developing intelligent, scalable systems for quadruped robots, enabling them to perform complex, real-world tasks with unprecedented versatility. Cheng's most notable contribution is the introduction of the Mixture of Robotic Experts (MoRE) model, a novel VLA framework that unlocks scalability in reinforcement learning for quadruped locomotion and manipulation. This breakthrough addresses a critical challenge in robotics: enabling smooth, adaptive performance across diverse actions and environments. With their 2025 paper already garnering 4 citations, Cheng's work is rapidly gaining recognition for its potential to transform autonomous robotic systems. Additionally, Cheng has contributed to interdisciplinary applications, authoring "Deep Learning: A Primer for Neurosurgeons" (2024), which bridges advanced AI techniques with medical practice. This dual expertise highlights Cheng's ability to translate complex technical innovations into impactful, real-world solutions. As a rising figure in robotics and AI, Cheng's research promises to redefine the capabilities of embodied agents.
Research Focus
Key Achievements
Top Papers
- 1
- 2Deep Learning: A Primer for Neurosurgeons2 citations · 2024