Problem
Research questions and friction points this paper is trying to address.
Multimodal Large Language Models
Urban Perception
Spatial Agency
Navigation
First-Person View
UrbanGround: From Local Perception to Spatial Agency in a Real-Scale City
🤖 AI Summary
本文通过构建UrbanGround平台，测试多模态大语言模型在复杂城市环境中将局部感知转化为可靠行动的能力，发现其在视觉识别和短程空间推理上有效，但在长距离导航和行人意识移动上表现不佳。
📝 Abstract
Multimodal large language models (MLLMs) can interpret a street view, but urban agency depends on whether such local evidence remains useful after the agent starts to move. In this paper, we investigate how far current MLLM agents can turn local urban perception into reliable action in a complicated real-scale city. We propose UrbanGround, the first sandbox to make this question testable in a physically constrained replica of Hong Kong built from territory-wide 3D geospatial data. UrbanGround supports closed-loop interaction from a first-person view and provides an interactive map for navigation. Agents can directly enter the 3D city and explore from a first-person view. Our analysis follows the growth of the spatial problem through three research questions. We first test whether an agent can ground a local scene well enough to answer spatial questions after active observation. Then we ask whether that grounding supports navigation as destinations become farther away and less explicit. Finally, we examine whether the resulting behavior survives changes in route availability and pedestrian motion. Contemporary MLLM agents usually show useful atomic abilities in visual recognition and short-range spatial reasoning, while orientation and pedestrian-aware movement remain unreliable. Their central failure emerges over extended exploration, where local abilities do not compose into sustained goal-directed behavior and errors accumulate without effective correction. We hope UrbanGround will support broader study of how far current MLLM agents can explore reliably in complex, open-ended urban environments.
Innovation
Methods, ideas, or system contributions that make the work stand out.
UrbanGround
first-person view
multimodal large language models
spatial reasoning
urban navigation
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