Yi‐Hung Chen

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About

Yi‐Hung Chen is a researcher at the forefront of intelligent robotics and control systems, with a particular focus on reinforcement learning for robotic manipulation. His most-cited work, "The Robotic Arm Velocity Planning Based on Reinforcement Learning" (2023), has garnered 8 citations, demonstrating early impact in a rapidly evolving field. Chen’s primary contribution lies in developing adaptive velocity planning algorithms that enable robotic arms to learn optimal motion trajectories through trial-and-error interactions, significantly improving efficiency and precision in dynamic environments. This work bridges the gap between traditional control theory and modern machine learning, offering practical solutions for industrial automation and collaborative robotics. By integrating reinforcement learning into velocity planning, Chen addresses critical challenges in real-time decision-making and obstacle avoidance, paving the way for more autonomous and responsive robotic systems. His research holds promise for applications in manufacturing, healthcare, and service robotics, where safe and efficient motion control is paramount. As a rising voice in the robotics community, Chen continues to explore how intelligent algorithms can unlock new levels of performance in robotic systems, making his contributions both timely and foundational for future innovations.