Zhirui Dai

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About

Zhirui Dai is a robotics researcher whose work bridges the gap between high-level language understanding and safe, real-world robot navigation. His primary research areas include task planning, semantic mapping, and safe control under uncertainty. Dai’s major contribution is the development of algorithms that enable autonomous robots to interpret natural language commands and execute them efficiently in complex environments. His most cited work, "Optimal Scene Graph Planning with Large Language Model Guidance" (2024, 17 citations), introduces a novel approach that leverages large language models to guide hierarchical metric-semantic planning, allowing robots to reason about objects, spaces, and tasks in a human-like manner. In his more recent work, "Sensor-based distributionally robust control for safe robot navigation in dynamic environments" (2025, 5 citations), Dai pioneers the use of distributionally robust optimization to create control barrier functions that guarantee probabilistic safety using only onboard sensors—a critical step toward deploying robots in unpredictable, human-populated spaces. With a growing citation impact and a focus on both theoretical rigor and practical deployment, Zhirui Dai is establishing himself as a rising figure in autonomous robotics, particularly at the intersection of semantic reasoning and safety-critical control.