Xinyu Qiu
Papers
2
Total Citations
20
H-Index
2
About
Xinyu Qiu is a researcher at the forefront of intelligent robotics, specializing in deep reinforcement learning for autonomous navigation in complex environments. Their major contribution lies in developing advanced obstacle avoidance algorithms tailored specifically for warehouse settings, where dynamic interactions between robots, goods, and personnel pose significant challenges. Qiu’s most-cited work, "Deep Reinforcement Learning-based Obstacle Avoidance for Robot Movement in Warehouse Environments" (2024), has garnered 17 citations, demonstrating its immediate impact. This research improves value function networks by incorporating pedestrian interaction data, enabling mobile robots to better prioritize and respond to their surroundings in real time. A subsequent paper (3 citations) further addresses the limitations of traditional robots in providing feedback on goods and pedestrians amidst cluttered warehouse layouts. Qiu’s work bridges the gap between theoretical reinforcement learning and practical logistics automation, offering scalable solutions for modern warehousing. Their achievements highlight a commitment to enhancing robot autonomy and safety, making them a notable contributor to the field of embodied AI and industrial robotics.
Research Focus
Key Achievements
Top Papers
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- 2