Yubin Chen

Papers

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About

Yubin Chen is an emerging researcher in the field of robotics and autonomous systems, with a primary focus on motion control and reinforcement learning for Automated Guided Vehicles (AGVs). Their most cited work, "The Determination of Reward Function in AGV Motion Control Based on DQN" (2022, 4 citations), makes a notable contribution by addressing a critical challenge in applying deep reinforcement learning to real-world robotics: the design of effective reward functions. Chen’s research demonstrates how Deep Q-Networks (DQN) can be leveraged to achieve more stable and reliable AGV motion, bridging the gap between theoretical reinforcement learning algorithms and practical industrial automation. This work is particularly relevant for students and researchers interested in the intersection of machine learning and robotics, as it provides a concrete methodology for improving autonomous navigation. While still early in their career, Chen’s focused approach to solving fundamental motion control problems positions them as a promising voice in the development of intelligent, self-navigating vehicles for logistics and manufacturing environments.