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About

Soyi Jung is a pioneering researcher at the intersection of robotics, artificial intelligence, and smart manufacturing, with a primary focus on developing intelligent autonomous systems capable of complex, human-like manipulation tasks. Her most significant contribution is the introduction of the Gaussian Random Trajectory guided Hierarchical Reinforcement Learning (GRT-HL) method for autonomous furniture assembly, a breakthrough that addresses the long-standing challenge of long-horizon planning in robotics. This work, which has garnered 9 citations, demonstrates how hierarchical learning can decompose intricate assembly sequences into manageable subtasks, enabling robots to operate with unprecedented autonomy. Dr. Jung has further advanced the field through her research on reinforcement learning-based motion planning for robotic manipulators in smart industry environments, and her comprehensive survey on behavior tree-based task planning algorithms provides a critical roadmap for integrating modular, scalable frameworks into autonomous robotic systems. Her work is instrumental in bridging the gap between theoretical AI and practical industrial automation, positioning her as a key contributor to the next generation of intelligent, adaptive manufacturing solutions.