Zhiqian Zhou
Papers
11
Total Citations
175
H-Index
5
About
Zhiqian Zhou is a robotics researcher whose work centers on enabling mobile robots to navigate safely and intelligently in complex, dynamic environments—particularly among human crowds. His major contributions lie at the intersection of deep reinforcement learning, online planning, and semantic perception for autonomous navigation. Zhou’s most influential work, “Robot navigation in a crowd by integrating deep reinforcement learning and online planning” (69 citations), introduces a hybrid framework that combines learning-based policies with real-time planning to handle the unpredictability of pedestrian-rich settings. He further advanced this area with “Navigating Robots in Dynamic Environment With Deep Reinforcement Learning” (40 citations), which addresses the pressing need for robots to replace human workers in high-risk tasks during the COVID-19 pandemic. Zhou also made notable strides in semantic SLAM for rescue robots (30 citations), enabling both geometric mapping and point-wise semantic labeling in disaster scenarios. His research on safe reinforcement learning (10 citations) tackles the critical requirement of collision-free motion in public spaces like airports and hospitals. With additional work on swarm robustness, terrain assessment, and pedestrian tracking, Zhou has established himself as a key figure in developing practical, safety-conscious navigation systems for real-world deployment.
Research Focus
Key Achievements
Top Papers
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- 2Navigating Robots in Dynamic Environment With Deep Reinforcement Learning40 citations · 2022
- 3Semantic RGB-D SLAM for Rescue Robot Navigation30 citations · 2020
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