Papers
2
Total Citations
9
H-Index
2
About
Yu-Sian Lin is a rising researcher in the fields of autonomous systems and intelligent robotics, with a focused expertise in deep reinforcement learning (DRL) for real-world control applications. Lin’s work bridges the gap between theoretical algorithms and practical deployment, addressing critical challenges in multi-agent coordination and robotic manipulation. Their most-cited paper, "Deep reinforcement learning–based collision avoidance strategy for multiple unmanned aerial vehicles" (2025, 5 citations), introduces a novel DRL framework that enables swarms of UAVs to navigate complex environments without collisions, a pivotal contribution for autonomous logistics and surveillance. Complementing this, Lin’s work on "Design and implementation of a soft Actor–Critic controller for a robotic arm" (2025, 4 citations) demonstrates how advanced actor-critic architectures can achieve smooth, adaptive control in precision tasks. Though early in their career, Lin’s publications have already garnered attention for their clear methodology and practical impact, signaling a promising trajectory in the integration of AI with physical systems. Their research is particularly notable for emphasizing safety and efficiency, making it highly relevant for students and engineers working on next-generation autonomous technologies.
Research Focus
Key Achievements
Top Papers
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