Papers
1
Total Citations
23
H-Index
1
About
Ze Guo is redefining human-robot collaboration through pioneering work in multimodal, socially-aware navigation. His research centers on bridging the gap between natural human communication and robotic action, particularly by integrating large language models (LLMs) with interactive sketching interfaces. In his landmark 2024 paper, "Language and Sketching: An LLM-driven Interactive Multimodal Multitask Robot Navigation Framework," Guo introduces a system that allows robots to understand and execute complex, multi-step commands—such as point-to-point navigation, human-following, and guiding—by combining verbal instructions with freehand sketches. This work, already garnering 23 citations, directly addresses a critical bottleneck in Human-Robot Interaction (HRI): the cumbersome and often ambiguous process of communicating commands. By enabling robots to interpret both language and visual cues simultaneously, Guo’s framework makes robot control more intuitive, flexible, and efficient. His contributions are not merely technical; they fundamentally advance the goal of creating robots that can seamlessly adapt to dynamic human environments, performing multiple tasks without requiring specialized programming. For students and researchers, Ze Guo’s work represents a vital step toward truly collaborative robots that understand us as naturally as we understand each other.
Research Focus
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