Hsi‐Yuan Chen

University of Florida, Universidade de Santiago de Compostela, National Taiwan University of Science and Technology

Papers

Total Citations

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About

Hsi-Yuan Chen is a robotics researcher whose work focuses on the intersection of control theory, computer vision, and deep reinforcement learning for autonomous mobile robots. His key contributions lie in multi-robot formation control, obstacle avoidance, and visual SLAM. Chen’s most impactful work, “Adaptive Leader-Follower Formation Control and Obstacle Avoidance via Deep Reinforcement Learning” (2019, 24 citations), introduces a novel DRL framework that decouples perception from control, enabling training without complex physics or 3D modeling—a practical advance for real-world deployment. He further advanced vision-based control with a switched systems approach (2017, 11 citations) that addresses the critical challenge of feature loss during tracking, providing dwell-time conditions for robust operation. His earlier work on RGB-D sensor-based 6DoF SLAM (2014) laid groundwork for indoor autonomous navigation using graph-based optimization. Collectively, Chen’s research demonstrates a systematic progression from foundational SLAM to adaptive, learning-based control, with his DRL methodology offering a scalable solution for coordinated multi-robot systems in dynamic environments.