# SOCIALGYM: A Framework for Benchmarking Social Robot Navigation

arXiv: https://arxiv.org/abs/2109.11011 · HTML: https://mlsanity.com/paper/2109.11011/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Sep 2021.

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

- [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)
- [SOCIALGYM 2.0: Simulator for Multi-Agent Social Robot Navigation in Shared Human Spaces](https://mlsanity.com/paper/2303.05584/) (2023)
- [Learning Social Navigation from Demonstrations with Conditional Neural Processes](https://mlsanity.com/paper/2210.03582/) (2022)
- [Arena-Rosnav 2.0: A Development and Benchmarking Platform for Robot Navigation in Highly Dynamic Environments](https://mlsanity.com/paper/2302.10023/) (2023)
- [SocNavGym: A Reinforcement Learning Gym for Social Navigation](https://mlsanity.com/paper/2304.14102/) (2023)
- [Decentralized Social Navigation with Non-Cooperative Robots via Bi-Level Optimization](https://mlsanity.com/paper/2306.08815/) (2023)
- [Stranger Danger! Identifying and Avoiding Unpredictable Pedestrians in RL-based Social Robot Navigation](https://mlsanity.com/paper/2407.06056/) (2024)
- [Socially aware navigation for mobile robots: a survey on deep reinforcement learning approaches](https://mlsanity.com/paper/2512.00049/) (2025)
- [Causality-enhanced Decision-Making for Autonomous Mobile Robots in Dynamic Environments](https://mlsanity.com/paper/2504.11901/) (2025)

## Abstract (arXiv)

Robots moving safely and in a socially compliant manner in dynamic human environments is an essential benchmark for long-term robot autonomy. However, it is not feasible to learn and benchmark social navigation behaviors entirely in the real world, as learning is data-intensive, and it is challenging to make safety guarantees during training. Therefore, simulation-based benchmarks that provide abstractions for social navigation are required. A framework for these benchmarks would need to support a wide variety of learning approaches, be extensible to the broad range of social navigation scenarios, and abstract away the perception problem to focus on social navigation explicitly. While there have been many proposed solutions, including high fidelity 3D simulators and grid world approximations, no existing solution satisfies all of the aforementioned properties for learning and evaluating social navigation behaviors. In this work, we propose SOCIALGYM, a lightweight 2D simulation environment for robot social navigation designed with extensibility in mind, and a benchmark scenario built on SOCIALGYM. Further, we present benchmark results that compare and contrast human-engineered and model-based learning approaches to a suite of off-the-shelf Learning from Demonstration (LfD) and Reinforcement Learning (RL) approaches applied to social robot navigation. These results demonstrate the data efficiency, task performance, social compliance, and environment transfer capabilities for each of the policies evaluated to provide a solid grounding for future social navigation research.