# SocialNav-SUB: Benchmarking VLMs for Scene Understanding in Social Robot Navigation

arXiv: https://arxiv.org/abs/2509.08757 · HTML: https://mlsanity.com/paper/2509.08757/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 32 of its 48 references that are in the map.
- First posted on arXiv 10 Sep 2025.

## Builds on (most cited first, 30 of 32)

- [DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning](https://mlsanity.com/paper/2501.12948/) (2025)
- [Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context](https://mlsanity.com/paper/2403.05530/) (2024)
- [VQA: Visual Question Answering](https://mlsanity.com/paper/1505.00468/) (2015)
- [SpatialVLM: Endowing Vision-Language Models with Spatial Reasoning Capabilities](https://mlsanity.com/paper/2401.12168/) (2024)
- [SpatialRGPT: Grounded Spatial Reasoning in Vision Language Models](https://mlsanity.com/paper/2406.01584/) (2024)
- [Set-of-Mark Prompting Unleashes Extraordinary Visual Grounding in GPT-4V](https://mlsanity.com/paper/2310.11441/) (2023)
- [Crowd-Robot Interaction: Crowd-aware Robot Navigation with Attention-based Deep Reinforcement Learning](https://mlsanity.com/paper/1809.08835/) (2018)
- [PhysBench: Benchmarking and Enhancing Vision-Language Models for Physical World Understanding](https://mlsanity.com/paper/2501.16411/) (2025)
- [Socially Aware Motion Planning with Deep Reinforcement Learning](https://mlsanity.com/paper/1703.08862/) (2017)
- [Core Challenges of Social Robot Navigation: A Survey](https://mlsanity.com/paper/2103.05668/) (2021)
- [ReMEmbR: Building and Reasoning Over Long-Horizon Spatio-Temporal Memory for Robot Navigation](https://mlsanity.com/paper/2409.13682/) (2024)
- [Does Spatial Cognition Emerge in Frontier Models?](https://mlsanity.com/paper/2410.06468/) (2024)
- [SACSoN: Scalable Autonomous Control for Social Navigation](https://mlsanity.com/paper/2306.01874/) (2023)
- [Principles and Guidelines for Evaluating Social Robot Navigation Algorithms](https://mlsanity.com/paper/2306.16740/) (2023)
- [Intention Aware Robot Crowd Navigation with Attention-Based Interaction Graph](https://mlsanity.com/paper/2203.01821/) (2022)
- [Guiding Long-Horizon Task and Motion Planning with Vision Language Models](https://mlsanity.com/paper/2410.02193/) (2024)
- [VLM-Social-Nav: Socially Aware Robot Navigation through Scoring using Vision-Language Models](https://mlsanity.com/paper/2404.00210/) (2024)
- [CoNVOI: Context-aware Navigation using Vision Language Models in Outdoor and Indoor Environments](https://mlsanity.com/paper/2403.15637/) (2024)
- [Sparkle: Mastering Basic Spatial Capabilities in Vision Language Models Elicits Generalization to Spatial Reasoning](https://mlsanity.com/paper/2410.16162/) (2024)
- [LeLaN: Learning A Language-Conditioned Navigation Policy from In-the-Wild Videos](https://mlsanity.com/paper/2410.03603/) (2024)
- [Toward Human-Like Social Robot Navigation: A Large-Scale, Multi-Modal, Social Human Navigation Dataset](https://mlsanity.com/paper/2303.14880/) (2023)
- [Probing Multimodal LLMs as World Models for Driving](https://mlsanity.com/paper/2405.05956/) (2024)
- [Embodied Scene Understanding for Vision Language Models via MetaVQA](https://mlsanity.com/paper/2501.09167/) (2025)
- [Social-LLaVA: Enhancing Robot Navigation through Human-Language Reasoning in Social Spaces](https://mlsanity.com/paper/2501.09024/) (2024)
- [Rethinking Social Robot Navigation: Leveraging the Best of Two Worlds](https://mlsanity.com/paper/2309.13466/) (2023)
- [VIVA: A Benchmark for Vision-Grounded Decision-Making with Human Values](https://mlsanity.com/paper/2407.03000/) (2024)
- [BehAV: Behavioral Rule Guided Autonomy Using VLMs for Robot Navigation in Outdoor Scenes](https://mlsanity.com/paper/2409.16484/) (2024)
- [HEIGHT: Heterogeneous Interaction Graph Transformer for Robot Navigation in Crowded and Constrained Environments](https://mlsanity.com/paper/2411.12150/) (2024)
- [CREStE: Scalable Mapless Navigation with Internet Scale Priors and Counterfactual Guidance](https://mlsanity.com/paper/2503.03921/) (2025)
- [HuBo-VLM: Unified Vision-Language Model designed for HUman roBOt interaction tasks](https://mlsanity.com/paper/2308.12537/) (2023)

## Built on by (most cited first, 10 of 10)

- [NaviTrace: Evaluating Embodied Navigation of Vision-Language Models](https://mlsanity.com/paper/2510.26909/) (2025)
- [TIC-VLA: A Think-in-Control Vision-Language-Action Model for Robot Navigation in Dynamic Environments](https://mlsanity.com/paper/2602.02459/) (2026)
- [MUSON: A Reasoning-oriented Multimodal Dataset for Socially Compliant Navigation in Urban Environments](https://mlsanity.com/paper/2512.22867/) (2025)
- [NormAct: Benchmarking Embodied Agents' Proactive Compliance with Unspoken Social Norms](https://mlsanity.com/paper/2606.27826/) (2026)
- [Towards Safer Mobile Agents: Scalable Generation and Evaluation of Diverse Scenarios for VLMs](https://mlsanity.com/paper/2601.08470/) (2026)
- [RobotValues: Evaluating Household Robots When Human Values Conflict](https://mlsanity.com/paper/2606.03312/) (2026)
- [Act on What You See: Unlocking Safe Social Navigation in Vision-Language-Action Models](https://mlsanity.com/paper/2606.10495/) (2026)
- [The World According to a Social Robot -- Augmenting Human-Robot Dialogue With Vision Language Models](https://mlsanity.com/paper/2607.16318/) (2026)
- [RobotEQ-Video: A Video-Centric Benchmark for Social Proactive Intelligence with World-State Taxonomy](https://mlsanity.com/paper/2609.21371/) (2026)
- [STARS: From Spatiotemporal Dynamics to Social Representations in Human-Robot Interaction](https://mlsanity.com/paper/2609.40245/) (2026)

## Abstract (arXiv)

Robot navigation in dynamic, human-centered environments requires socially-compliant decisions grounded in robust scene understanding. Recent Vision-Language Models (VLMs) exhibit promising capabilities such as object recognition, common-sense reasoning, and contextual understanding-capabilities that align with the nuanced requirements of social robot navigation. However, it remains unclear whether VLMs can accurately understand complex social navigation scenes (e.g., inferring the spatial-temporal relations among agents and human intentions), which is essential for safe and socially compliant robot navigation. While some recent works have explored the use of VLMs in social robot navigation, no existing work systematically evaluates their ability to meet these necessary conditions. In this paper, we introduce the Social Navigation Scene Understanding Benchmark (SocialNav-SUB), a Visual Question Answering (VQA) dataset and benchmark designed to evaluate VLMs for scene understanding in real-world social robot navigation scenarios. SocialNav-SUB provides a unified framework for evaluating VLMs against human and rule-based baselines across VQA tasks requiring spatial, spatiotemporal, and social reasoning in social robot navigation. Through experiments with state-of-the-art VLMs, we find that while the best-performing VLM achieves an encouraging probability of agreeing with human answers, it still underperforms simpler rule-based approach and human consensus baselines, indicating critical gaps in social scene understanding of current VLMs. Our benchmark sets the stage for further research on foundation models for social robot navigation, offering a framework to explore how VLMs can be tailored to meet real-world social robot navigation needs. An overview of this paper along with the code and data can be found at https://larg.github.io/socialnav-sub .