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Weimin Zhuang

Publications and source records attributed to Weimin Zhuang.

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CALM: Configuration-Aware Human Intervention Boundaries During Robot Approach

How robot body configuration shapes human intervention during approach remains underexplored. We conducted a within-participants study with 41 participants, measuring final stopping distance, subjective comfort, and exploratory eye-tracking responses across four humanoid arm configurations and two spatial scales. Full forward arm extension increased stopping distance by approximately 31-36 cm relative to arms-down. Spatial scale primarily affected comfort and pupil responses without a detectable stopping-distance shift. We introduce the Configuration-Aware Limit Model (CALM), which translates stopping-distance distributions into configuration-dependent population-coverage boundaries. Estimated boundaries at 80% coverage ranged from 0.88 to 1.47 m. In an illustrative one-dimensional planning analysis, reconfiguration enabled a 1.10 m approach goal that was unreachable with arms remaining fully extended under the same nominal pointwise 20% intervention-probability constraint. These findings support treating body configuration as a planning variable while distinguishing physical safety, behavioral intervention, and subjective cost.

cs.RO

VLM-based automatic multi-granularity graph representation of building layouts for design informatics

Architectural floorplan images encode rich relational knowledge among functional spaces, which underpins design retrieval, knowledge-based reasoning, and BIM enrichment through the building lifecycle. However, it remains challenging to automatically construct task-adaptive graph representations for public buildings. To address this gap, we first define a multi-granularity Level-of-Graphs (LoGs) for public building layouts. Methodologically, we present a Vision-Language Model (VLM)-based automatic LoG construction through node identification, edge inference, text parsing, and graph coarsening. VLM-generated representations are systematically evaluated and tested in real-world tasks, using 147 academic library floorplans worldwide as a case study. Experiments showed VLM-generated graphs were broadly consistent with human-labeled graphs (matched node ratio >= 92%; 509.3 s per floor plan for three-LoG graph generation). Meso-grained graphs yield the best node-level zone prediction (Macro F1 = 0.647, at 65% of fine-grained complexity), while coarse-grained graphs are most effective for graph-level layout quality evaluation (Spearman's \r{ho} = 0.610, at 16% of fine-grained complexity). By enabling scalable, annotation-free extraction of structured layout information from floorplan images, this study advances design informatics by converting plan images into knowledge representations, thereby enhancing the utilization of design information across the building life cycle.

cs.AI