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This study uses persona-conditioned multimodal large language models to analyze how robot presence influences perceived safety in urban environments, revealing distinct biases between model judgments and human responses regarding delivery versus humanoid robots. The research highlights critical nuances in AI perception systems, showing that synthetic personas capture only partial aspects of human judgment when evaluating robotic threats or obstacles at night. These findings are essential for developers deploying autonomous agents in public spaces to ensure their safety assessments align with real-world human expectations.
Robots in the City: Persona-Based Perceptions of Urban Robots
arXiv:2611.05954v1 Announce Type: cross Abstract: Pedestrians increasingly encounter robots in urban public spaces without interacting with them, but how robots change the perceived safety of a street remains unclear. We examine how robot presence and type affect perceived safety across urban contexts. We generate 18 images of Seoul streets that vary in time of day, road class, and robot condition (no robot, a delivery robot, or a humanoid). Two multimodal large language models (MLLMs), Qwen3.8-27B and Gemma4-26B, each conditioned on 500 synthetic personas, choose the safer image in each pair, and 25 human respondents complete the same task. Both models prefer streets without robots and favor the delivery robot over the humanoid. Robots are chosen more often at night and less often by older personas, whereas road class has smaller effects. In the model rationales, the delivery robot appears as a physical obstacle, whereas the humanoid appears as either a threat or a source of protection. Like humans, both models chose robots more often at night, but chose the humanoid over the delivery robot far less often, and Gemma showed a night effect for the humanoid about three times that of humans. These results show that the effect of a robot on perceived safety depends on when it appears, and that persona-conditioned MLLMs capture only part of human judgments.
Abstract:Pedestrians increasingly encounter robots in urban public spaces without interacting with them, but how robots change the perceived safety of a street remains unclear. We examine how robot presence and type affect perceived safety across urban contexts. We generate 18 images of Seoul streets that vary in time of day, road class, and robot condition (no robot, a delivery robot, or a humanoid). Two multimodal large language models (MLLMs), Qwen3.8-27B and Gemma4-26B, each conditioned on 500 synthetic personas, choose the safer image in each pair, and 25 human respondents complete the same task. Both models prefer streets without robots and favor the delivery robot over the humanoid. Robots are chosen more often at night and less often by older personas, whereas road class has smaller effects. In the model rationales, the delivery robot appears as a physical obstacle, whereas the humanoid appears as either a threat or a source of protection. Like humans, both models chose robots more often at night, but chose the humanoid over the delivery robot far less often, and Gemma showed a night effect for the humanoid about three times that of humans. These results show that the effect of a robot on perceived safety depends on when it appears, and that persona-conditioned MLLMs capture only part of human judgments.
Human-Computer Interaction (cs.HC); Computation and Language (cs.CL); Robotics (cs.RO)
arXiv:2611.05954 [cs.HC]
(or arXiv:2611.05954v1 [cs.HC] for this version)
https://doi.org/10.48550/arXiv.2611.05954 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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