# Arena-Rosnav: Towards Deployment of Deep-Reinforcement-Learning-Based Obstacle Avoidance into Conventional Autonomous Navigation Systems

arXiv: https://arxiv.org/abs/2104.03616 · HTML: https://mlsanity.com/paper/2104.03616/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 22 references that are in the map.
- First posted on arXiv 8 Apr 2021.

## Builds on (most cited first, 8 of 8)

- [Crowd-Robot Interaction: Crowd-aware Robot Navigation with Attention-based Deep Reinforcement Learning](https://mlsanity.com/paper/1809.08835/) (2018)
- [Socially Aware Motion Planning with Deep Reinforcement Learning](https://mlsanity.com/paper/1703.08862/) (2017)
- [Motion Planning Among Dynamic, Decision-Making Agents with Deep Reinforcement Learning](https://mlsanity.com/paper/1805.01956/) (2018)
- [Learning Navigation Behaviors End-to-End with AutoRL](https://mlsanity.com/paper/1809.10124/) (2018)
- [PRM-RL: Long-range Robotic Navigation Tasks by Combining Reinforcement Learning and Sampling-based Planning](https://mlsanity.com/paper/1710.03937/) (2017)
- [Long-Range Indoor Navigation with PRM-RL](https://mlsanity.com/paper/1902.09458/) (2019)
- [NavRep: Unsupervised Representations for Reinforcement Learning of Robot Navigation in Dynamic Human Environments](https://mlsanity.com/paper/2012.04406/) (2020)
- [Toward Verifiable Real-Time Obstacle Motion Prediction for Dynamic Collision Avoidance](https://mlsanity.com/paper/1811.01075/) (2018)

## Built on by (most cited first, 15 of 15)

- [Diffusion-Reinforcement Learning Hierarchical Motion Planning in Multi-agent Adversarial Games](https://mlsanity.com/paper/2403.10794/) (2024)
- [GSON: A Group-based Social Navigation Framework with Large Multimodal Model](https://mlsanity.com/paper/2409.18084/) (2024)
- [Enhancing Navigational Safety in Crowded Environments using Semantic-Deep-Reinforcement-Learning-based Navigation](https://mlsanity.com/paper/2109.11288/) (2021)
- [Enhanced method for reinforcement learning based dynamic obstacle avoidance by assessment of collision risk](https://mlsanity.com/paper/2212.04123/) (2022)
- [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)
- [Arena-Rosnav 2.0: A Development and Benchmarking Platform for Robot Navigation in Highly Dynamic Environments](https://mlsanity.com/paper/2302.10023/) (2023)
- [Holistic Deep-Reinforcement-Learning-based Training of Autonomous Navigation Systems](https://mlsanity.com/paper/2302.02921/) (2023)
- [Embodied Escaping: End-to-End Reinforcement Learning for Robot Navigation in Narrow Environment](https://mlsanity.com/paper/2503.03208/) (2025)
- [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)
- [Virtual Guidance as a Mid-level Representation for Navigation with Augmented Reality](https://mlsanity.com/paper/2303.02731/) (2023)
- [Evolutionary Curriculum Training for DRL-Based Navigation Systems](https://mlsanity.com/paper/2306.08870/) (2023)
- [RUMOR: Reinforcement learning for Understanding a Model of the Real World for Navigation in Dynamic Environments](https://mlsanity.com/paper/2404.16672/) (2024)
- [MAER-Nav: Bidirectional Motion Learning Through Mirror-Augmented Experience Replay for Robot Navigation](https://mlsanity.com/paper/2503.23908/) (2025)
- [Real-Time Adaptive Motion Planning via Point Cloud-Guided, Energy-Based Diffusion and Potential Fields](https://mlsanity.com/paper/2507.09383/) (2025)

## Abstract (arXiv)

Recently, mobile robots have become important tools in various industries, especially in logistics. Deep reinforcement learning emerged as an alternative planning method to replace overly conservative approaches and promises more efficient and flexible navigation. However, deep reinforcement learning approaches are not suitable for long-range navigation due to their proneness to local minima and lack of long term memory, which hinders its widespread integration into industrial applications of mobile robotics. In this paper, we propose a navigation system incorporating deep-reinforcement-learning-based local planners into conventional navigation stacks for long-range navigation. Therefore, a framework for training and testing the deep reinforcement learning algorithms along with classic approaches is presented. We evaluated our deep-reinforcement-learning-enhanced navigation system against various conventional planners and found that our system outperforms them in terms of safety, efficiency and robustness.