Genjin Xie
Papers
2
Total Citations
85
H-Index
2
About
Genjin Xie is a leading researcher at the intersection of optimal control, reinforcement learning (RL), and autonomous systems. His work is distinguished by a focus on bridging the gap between theoretical control methods and practical, real-world deployment, particularly in autonomous driving and industrial automation. Xie’s most impactful contribution is the development of **GOPS (General Optimal Control Problem Solver)**, a comprehensive framework designed to overcome the heavy online computational burdens of traditional Model Predictive Control. This work, with 62 citations, provides a unified platform that integrates modern RL with classical control, making advanced control strategies more accessible for industrial tasks. Furthering the cause of safe AI, Xie introduced the **Separated Proportional-Integral Lagrangian** method for model-based chance-constrained RL. This approach elegantly manages probabilistic safety constraints under uncertainty, addressing a critical limitation of existing penalty and Lagrangian methods. With 23 citations, this work is foundational for deploying RL in high-stakes environments where safety is paramount. Through these innovations, Xie is actively shaping the next generation of robust, efficient, and safe autonomous systems.
Research Focus
Key Achievements
Top Papers
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- 2