Robots in the City: Persona-Based Perceptions of Urban Robots
Read the original on arXiv Computation and Language →The Flow has not summarised this story yet — read it at arXiv Computation and Language.
The Flow has not summarised this story yet — read it at arXiv Computation and Language.
arXiv:2503.00610v2 Announce Type: replace-cross Abstract: Understanding how people perceive urban environments is essential for inclusive planning, yet conventional surveys are costly and difficult t...
The paper investigates how persona prompting influences the language produced by two multimodal large language models—Qwen3‑VL and Gemma4—when describing urban scenes. Outputs are categorized into descriptive grounding (captions), perception tags, and interpretive framing (justifications). Results show captions largely converge across persona profiles, while justifications differ markedly, especially along economic status, political orientation, and personality dimensions, with economic status producing the greatest variation. Perception tags also reflect attribute similarities, and exploratory topic analysis indicates persona‑specific evaluative emphasis. Overall, persona prompting has a stronger effect on interpretive framing than on descriptive grounding.
Assessing proxemic danger from a robot's egocentric perspective is critical for safe embodied navigation in human environments and requires both visual and contextual reasoning. We evaluate three opensource vision-language models (VLMs) (\textit{InternVL}, \textit{Qwen-VL}, and \textit{SmolVLM}) on the classification of egocentric robot images into four danger levels, comparing three prompting strategies and two rounds of QLoRA fine-tuning against a stratified random baseline.
arXiv:2607. 24113v1 Announce Type: cross Abstract: Previous research has shown that a human-like robot's acceptance heavily depends on the setting in which it operates and its ability to perform relevant tasks.
UrbanGround is a sandbox that tests how well multimodal large language model agents can translate local street‑view perception into reliable action within a physically realistic replica of Hong Kong. The platform offers closed‑loop first‑person interaction and an interactive map, allowing agents to navigate the 3D city and answer spatial questions. The study evaluates agents across three research questions—scene grounding, navigation over increasing distances, and robustness to route changes—revealing that while agents excel at visual recognition and short‑range reasoning, they struggle with sustained goal‑directed behavior and pedestrian‑aware movement.
arXiv:2607. 14631v1 Announce Type: cross Abstract: Inferring apparent personality from facial images is important in social scenarios for embodied agents in human-robot interaction.