# SocNavBench: A Grounded Simulation Testing Framework for Evaluating Social Navigation

arXiv: https://arxiv.org/abs/2103.00047 · HTML: https://mlsanity.com/paper/2103.00047/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Feb 2021.

## Built on by (most cited first, 24 of 24)

- [Habitat 3.0: A Co-Habitat for Humans, Avatars and Robots](https://mlsanity.com/paper/2310.13724/) (2023)
- [Socially Compliant Navigation Dataset (SCAND): A Large-Scale Dataset of Demonstrations for Social Navigation](https://mlsanity.com/paper/2203.15041/) (2022)
- [Principles and Guidelines for Evaluating Social Robot Navigation Algorithms](https://mlsanity.com/paper/2306.16740/) (2023)
- [MetaUrban: An Embodied AI Simulation Platform for Urban Micromobility](https://mlsanity.com/paper/2407.08725/) (2024)
- [SOCIALMAPF: Optimal and Efficient Multi-Agent Path Finding with Strategic Agents for Social Navigation](https://mlsanity.com/paper/2210.08390/) (2022)
- [TBD Pedestrian Data Collection: Towards Rich, Portable, and Large-Scale Natural Pedestrian Data](https://mlsanity.com/paper/2309.17187/) (2023)
- [GSON: A Group-based Social Navigation Framework with Large Multimodal Model](https://mlsanity.com/paper/2409.18084/) (2024)
- [SimWorld-Robotics: Synthesizing Photorealistic and Dynamic Urban Environments for Multimodal Robot Navigation and Collaboration](https://mlsanity.com/paper/2512.10046/) (2025)
- [SOCIALGYM 2.0: Simulator for Multi-Agent Social Robot Navigation in Shared Human Spaces](https://mlsanity.com/paper/2303.05584/) (2023)
- [Optimizing Algorithms From Pairwise User Preferences](https://mlsanity.com/paper/2308.04571/) (2023)
- [Learning Social Navigation from Demonstrations with Conditional Neural Processes](https://mlsanity.com/paper/2210.03582/) (2022)
- [Mixed Strategy Nash Equilibrium for Crowd Navigation](https://mlsanity.com/paper/2403.01537/) (2024)
- [Decentralized Social Navigation with Non-Cooperative Robots via Bi-Level Optimization](https://mlsanity.com/paper/2306.08815/) (2023)
- [A Framework for Realistic Simulation of Daily Human Activity](https://mlsanity.com/paper/2311.15400/) (2023)
- [Recent Advances in Multi-Agent Human Trajectory Prediction: A Comprehensive Review](https://mlsanity.com/paper/2506.14831/) (2025)
- [LISN: Language-Instructed Social Navigation with VLM-based Controller Modulating](https://mlsanity.com/paper/2512.09920/) (2025)
- [IR-SIM: A Lightweight Declarative Simulator for Navigation Learning and Benchmarking](https://mlsanity.com/paper/2606.08729/) (2026)
- [Towards Rich, Portable, and Large-Scale Pedestrian Data Collection](https://mlsanity.com/paper/2203.01974/) (2022)
- [Learned Tree Search for Long-Horizon Social Robot Navigation in Shared Airspace](https://mlsanity.com/paper/2304.01428/) (2023)
- [SocRATES: Towards Automated Scenario-based Testing of Social Navigation Algorithms](https://mlsanity.com/paper/2412.19595/) (2024)
- [SPLC: Social Preference Learning for Crowd Robot Navigation](https://mlsanity.com/paper/2607.01925/) (2026)
- [Commonsense-Grounded Path Planning from Abstract Instructions](https://mlsanity.com/paper/2609.22813/) (2026)
- [Do World Models Make Better Robots? A Survey of Evaluation Benchmarks for Predictive Embodied Intelligence](https://mlsanity.com/paper/2609.29669/) (2026)
- [Learning Social Navigation from Internet Videos in the Policy State Space](https://mlsanity.com/paper/2609.37476/) (2026)

## Abstract (arXiv)

The human-robot interaction (HRI) community has developed many methods for robots to navigate safely and socially alongside humans. However, experimental procedures to evaluate these works are usually constructed on a per-method basis. Such disparate evaluations make it difficult to compare the performance of such methods across the literature. To bridge this gap, we introduce SocNavBench, a simulation framework for evaluating social navigation algorithms. SocNavBench comprises a simulator with photo-realistic capabilities and curated social navigation scenarios grounded in real-world pedestrian data. We also provide an implementation of a suite of metrics to quantify the performance of navigation algorithms on these scenarios. Altogether, SocNavBench provides a test framework for evaluating disparate social navigation methods in a consistent and interpretable manner. To illustrate its use, we demonstrate testing three existing social navigation methods and a baseline method on SocNavBench, showing how the suite of metrics helps infer their performance trade-offs. Our code is open-source, allowing the addition of new scenarios and metrics by the community to help evolve SocNavBench to reflect advancements in our understanding of social navigation.