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About

Yuqian Jiang is a robotics and artificial intelligence researcher whose work centers on task planning, human-robot interaction, and autonomous decision-making for service robots. With a research portfolio accumulating over 240 citations, Jiang has made substantial contributions to the challenge of enabling robots to operate intelligently in complex, real-world environments. Among Jiang's most influential contributions is a systematic empirical comparison of PDDL- and ASP-based task planning systems (67 citations), offering practitioners critical guidance in selecting appropriate planners for diverse robotics applications. Equally notable is pioneering work on task-motion planning integrated with reinforcement learning, enabling mobile service robots to adaptively generate executable plans while accounting for uncertain real-world conditions. Jiang also advanced human-robot communication by developing dialog-driven methods that jointly improve language parsing and perceptual understanding, allowing robots to interpret natural language commands more reliably (46 citations). Beyond individual robot behavior, Jiang has tackled multi-robot coordination under temporal uncertainty and resource conflicts, contributing frameworks that optimize collaborative planning across robot teams. Work on open-world reasoning further addresses the practical challenge of robots encountering objects or situations absent from their knowledge base. Together, these contributions position Jiang as a versatile researcher bridging symbolic AI, machine learning, and practical robotics systems design.