Zhongye Xie
Papers
2
Total Citations
7
H-Index
2
About
Zhongye Xie is a rising researcher at the forefront of intelligent control systems, specializing in deep reinforcement learning (DRL) for robotic manipulation. His work directly tackles the critical challenge of achieving precise trajectory tracking in complex, uncertain environments where traditional controllers fail. Xie’s major contributions include the development of the Curiosity Model Policy Optimization (CMPO) framework, which ingeniously integrates curiosity-driven exploration with model-based RL to overcome input saturation and environmental uncertainty. He has also pioneered a novel approach combining deep RL with ensemble random network distillation, transforming the notoriously difficult task of robotic manipulator tracking into a solvable dense reward problem. While his most-cited papers—garnering 4 and 3 citations respectively from 2024—are early in their impact trajectory, they represent foundational steps toward more robust, adaptive, and intelligent robotic systems. Xie’s work is particularly notable for its practical focus on real-world control constraints, bridging the gap between theoretical RL algorithms and the physical limitations of robotic hardware. For students and researchers, his research offers a compelling blueprint for building resilient control policies that can learn and adapt in the wild.
Research Focus
Key Achievements
Top Papers
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- 2