# Arena-Bench: A Benchmarking Suite for Obstacle Avoidance Approaches in Highly Dynamic Environments

arXiv: https://arxiv.org/abs/2206.05728 · HTML: https://mlsanity.com/paper/2206.05728/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jun 2022.

## Built on by (most cited first, 9 of 9)

- [Principles and Guidelines for Evaluating Social Robot Navigation Algorithms](https://mlsanity.com/paper/2306.16740/) (2023)
- [Arena 4.0: A Comprehensive ROS2 Development and Benchmarking Platform for Human-centric Navigation Using Generative-Model-based Environment Generation](https://mlsanity.com/paper/2409.12471/) (2024)
- [How Social is It? A Benchmark for LLMs' Capabilities in Multi-user Multi-turn Social Agent Tasks](https://mlsanity.com/paper/2505.04628/) (2025)
- [Arena-Rosnav 2.0: A Development and Benchmarking Platform for Robot Navigation in Highly Dynamic Environments](https://mlsanity.com/paper/2302.10023/) (2023)
- [FLUX: Accelerating Cross-Embodiment Generative Navigation Policies via Rectified Flow and Static-to-Dynamic Learning](https://mlsanity.com/paper/2603.12806/) (2026)
- [Deep-Reinforcement-Learning-based Path Planning for Industrial Robots using Distance Sensors as Observation](https://mlsanity.com/paper/2301.05980/) (2023)
- [Arena-Web -- A Web-based Development and Benchmarking Platform for Autonomous Navigation Approaches](https://mlsanity.com/paper/2302.02898/) (2023)
- [RUMOR: Reinforcement learning for Understanding a Model of the Real World for Navigation in Dynamic Environments](https://mlsanity.com/paper/2404.16672/) (2024)
- [Improving the Generalization of Unseen Crowd Behaviors for Reinforcement Learning based Local Motion Planners](https://mlsanity.com/paper/2410.12232/) (2024)

## Abstract (arXiv)

The ability to autonomously navigate safely, especially within dynamic environments, is paramount for mobile robotics. In recent years, DRL approaches have shown superior performance in dynamic obstacle avoidance. However, these learning-based approaches are often developed in specially designed simulation environments and are hard to test against conventional planning approaches. Furthermore, the integration and deployment of these approaches into real robotic platforms are not yet completely solved. In this paper, we present Arena-bench, a benchmark suite to train, test, and evaluate navigation planners on different robotic platforms within 3D environments. It provides tools to design and generate highly dynamic evaluation worlds, scenarios, and tasks for autonomous navigation and is fully integrated into the robot operating system. To demonstrate the functionalities of our suite, we trained a DRL agent on our platform and compared it against a variety of existing different model-based and learning-based navigation approaches on a variety of relevant metrics. Finally, we deployed the approaches towards real robots and demonstrated the reproducibility of the results. The code is publicly available at github.com/ignc-research/arena-bench.