Zixuan Guo

Beijing Institute of Technology, Chinese University of Hong Kong

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About

Zixuan Guo is a robotics researcher advancing the frontier of autonomous navigation under complex, real-world constraints. Their work focuses on integrating formal logic specifications—specifically Signal Temporal Logic (STL) and Linear Temporal Logic (LTL)—into motion planning for robots operating in partially unknown or multi-task environments. Guo’s most cited paper, “Two-Phase Motion Planning Under Signal Temporal Logic Specifications in Partially Unknown Environments” (2022, 19 citations), tackles a critical gap: enabling robots to satisfy high-level temporal goals without a fully pre-mapped world, a practical necessity for field robotics. They further developed an interactive system for multiple-task LTL path planning (2023), bridging the gap between theoretical logic and user-friendly robot instruction. In their work on dual-stage path planning for active pose-graph SLAM (2022), Guo addressed the challenge of simultaneous exploration and uncertainty reduction in 3D environments, a key problem for autonomous mapping. By combining formal methods with practical planning algorithms, Guo’s research is shaping how robots can reason about time-sensitive tasks and adapt to unknown spaces—essential contributions for applications in search-and-rescue, autonomous inspection, and human-robot collaboration.