Chaeeun Yang

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About

Dr. Chaeeun Yang is pioneering the integration of formal logic and deep learning for autonomous robotic navigation. Her primary research focuses on path planning under complex mission constraints, particularly using Linear Temporal Logic (LTL) to specify high-level tasks. In her landmark 2024 paper, "End-to-End Path Planning Under Linear Temporal Logic Specifications," Dr. Yang introduced a novel deep learning framework that unifies LTL satisfaction with trajectory optimization in a single, end-to-end trainable neural network. This innovation eliminates the traditional separation between task planning and motion control, enabling robots to efficiently generate paths that meet intricate temporal and safety specifications. While early in her career, this work has already garnered attention (2 citations), positioning her as a rising leader in formal-methods-driven robotics. Her approach promises to advance autonomous systems in applications like warehouse logistics, search-and-rescue, and autonomous driving, where robots must reliably follow complex, time-sensitive instructions. Dr. Yang’s contributions are shaping a new paradigm where logical reasoning and learning converge for more capable, trustworthy robots.