# A Protocol for Validating Social Navigation Policies

arXiv: https://arxiv.org/abs/2204.05443 · HTML: https://mlsanity.com/paper/2204.05443/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Apr 2022.

## Built on by (most cited first, 12 of 12)

- [Principles and Guidelines for Evaluating Social Robot Navigation Algorithms](https://mlsanity.com/paper/2306.16740/) (2023)
- [VLM-Social-Nav: Socially Aware Robot Navigation through Scoring using Vision-Language Models](https://mlsanity.com/paper/2404.00210/) (2024)
- [Learning Model Predictive Controllers with Real-Time Attention for Real-World Navigation](https://mlsanity.com/paper/2209.10780/) (2022)
- [Social-LLaVA: Enhancing Robot Navigation through Human-Language Reasoning in Social Spaces](https://mlsanity.com/paper/2501.09024/) (2024)
- [Exploiting Proximity-Aware Tasks for Embodied Social Navigation](https://mlsanity.com/paper/2212.00767/) (2022)
- [A Study on Learning Social Robot Navigation with Multimodal Perception](https://mlsanity.com/paper/2309.12568/) (2023)
- [Gesture2Path: Imitation Learning for Gesture-aware Navigation](https://mlsanity.com/paper/2209.09375/) (2022)
- [Predicting Human Perceptions of Robot Performance During Navigation Tasks](https://mlsanity.com/paper/2310.11590/) (2023)
- [Narrate2Nav: Real-Time Visual Navigation with Implicit Language Reasoning in Human-Centric Environments](https://mlsanity.com/paper/2506.14233/) (2025)
- [Few-Shot Inference of Human Perceptions of Robot Performance in Social Navigation Scenarios](https://mlsanity.com/paper/2512.16019/) (2025)
- [Vision-Language Models for Deployable Social Robot Navigation: Bridging Semantic Reasoning and Low-Level Control](https://mlsanity.com/paper/2606.28760/) (2026)
- [ACME: A Multi-Cultural, Multi-Embodiment Social-Navigation Dataset](https://mlsanity.com/paper/2607.21964/) (2026)

## Abstract (arXiv)

Enabling socially acceptable behavior for situated agents is a major goal of recent robotics research. Robots should not only operate safely around humans, but also abide by complex social norms. A key challenge for developing socially-compliant policies is measuring the quality of their behavior. Social behavior is enormously complex, making it difficult to create reliable metrics to gauge the performance of algorithms. In this paper, we propose a protocol for social navigation benchmarking that defines a set of canonical social navigation scenarios and an in-situ metric for evaluating performance on these scenarios using questionnaires. Our experiments show this protocol is realistic, scalable, and repeatable across runs and physical spaces. Our protocol can be replicated verbatim or it can be used to define a social navigation benchmark for novel scenarios. Our goal is to introduce a protocol for benchmarking social scenarios that is homogeneous and comparable.