From Obstacles to Etiquette: Robot Social Navigation with VLM-Informed Path Selection

• URL: http://arxiv.org/abs/2602.09002v1
• Date: Mon, 09 Feb 2026 18:46:12 GMT
• Title: From Obstacles to Etiquette: Robot Social Navigation with VLM-Informed Path Selection
• Authors: Zilin Fang, Anxing Xiao, David Hsu, Gim Hee Lee,
• Abstract summary: This paper presents a social robot navigation framework that integrates geometric planning with contextual social reasoning.<n>The system first extracts obstacles and human dynamics to generate geometrically feasible candidate paths, then leverages a fine-tuned vision-language model (VLM) to evaluate these paths.<n>Experiments in four social navigation contexts demonstrate that our method achieves the best overall performance with the lowest personal space violation duration, the minimal pedestrian-facing time, and no social zone intrusions.
• Score: 57.74400052368147
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Navigating socially in human environments requires more than satisfying geometric constraints, as collision-free paths may still interfere with ongoing activities or conflict with social norms. Addressing this challenge calls for analyzing interactions between agents and incorporating common-sense reasoning into planning. This paper presents a social robot navigation framework that integrates geometric planning with contextual social reasoning. The system first extracts obstacles and human dynamics to generate geometrically feasible candidate paths, then leverages a fine-tuned vision-language model (VLM) to evaluate these paths, informed by contextually grounded social expectations, selecting a socially optimized path for the controller. This task-specific VLM distills social reasoning from large foundation models into a smaller and efficient model, allowing the framework to perform real-time adaptation in diverse human-robot interaction contexts. Experiments in four social navigation contexts demonstrate that our method achieves the best overall performance with the lowest personal space violation duration, the minimal pedestrian-facing time, and no social zone intrusions. Project page: https://path-etiquette.github.io

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