Ruiqi Cong
Papers
2
Total Citations
14
H-Index
2
About
Ruiqi Cong is a pioneering researcher in the field of modular robot manipulator (MRM) systems, with a focus on advanced control theory and human-robot interaction. Their work centers on developing sophisticated optimal control strategies that bridge game theory, safety constraints, and physical human-robot collaboration. Cong’s most notable contribution is the introduction of a Stackelberg-Pareto differential game-based approximate optimal control approach for user-led MRM systems, which enables hierarchical interaction control through joint torque feedback technology—a framework that has already garnered 11 citations since its 2025 publication. Additionally, Cong has advanced safe control methodologies by integrating control barrier functions into nonzero-sum game formulations, achieving approximate optimal control while ensuring operational safety in complex manipulator dynamics (3 citations). These contributions are particularly significant for applications requiring intuitive human-robot teamwork, such as manufacturing and assistive robotics. By combining differential game theory with practical safety guarantees, Cong’s research provides a rigorous foundation for next-generation modular robots that can adapt to human intent while maintaining robust performance. Their work represents a critical step toward intelligent, user-centric robotic systems.
Research Focus
Key Achievements
Top Papers
- 1
- 2