Yongcheng Cui

Papers

Total Citations

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About

Yongcheng Cui is a researcher advancing the field of robotics through the integration of deep reinforcement learning and active perception. His primary research areas include active object detection (AOD) for service robots, deep Q-learning networks, and intelligent decision-making algorithms for autonomous systems. Cui’s most notable contribution is the development of a deep Q-learning network-based active object detection model, accompanied by a novel training algorithm that enables service robots to efficiently locate and approach target objects in dynamic home environments. This work directly addresses a critical challenge in domestic robotics: enabling machines to take purposeful, adaptive movements rather than relying on passive sensing. With 12 citations to his flagship 2022 paper, Cui’s research is gaining traction among scholars working on robot autonomy and human-robot interaction. His approach stands out for bridging the gap between reinforcement learning theory and practical robotic applications, offering a scalable framework that improves both detection accuracy and operational efficiency. Cui’s work is particularly relevant for students and researchers interested in embodied AI, where robots must learn to interact with their surroundings through trial and error.