# All-in-One: A DRL-based Control Switch Combining State-of-the-art Navigation Planners

arXiv: https://arxiv.org/abs/2109.11636 · HTML: https://mlsanity.com/paper/2109.11636/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Sep 2021.

## Built on by (most cited first, 8 of 8)

- [Variational Curriculum Reinforcement Learning for Unsupervised Discovery of Skills](https://mlsanity.com/paper/2310.19424/) (2023)
- [DR-MPC: Deep Residual Model Predictive Control for Real-world Social Navigation](https://mlsanity.com/paper/2410.10646/) (2024)
- [Learning to navigate efficiently and precisely in real environments](https://mlsanity.com/paper/2401.14349/) (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)
- [Deep-Reinforcement-Learning-based Path Planning for Industrial Robots using Distance Sensors as Observation](https://mlsanity.com/paper/2301.05980/) (2023)
- [Evolutionary Curriculum Training for DRL-Based Navigation Systems](https://mlsanity.com/paper/2306.08870/) (2023)
- [When to Plan: Learning to Select Between Reactive Control and Deliberative Planning](https://mlsanity.com/paper/2607.16421/) (2026)

## Abstract (arXiv)

Autonomous navigation of mobile robots is an essential aspect in use cases such as delivery, assistance or logistics. Although traditional planning methods are well integrated into existing navigation systems, they struggle in highly dynamic environments. On the other hand, Deep-Reinforcement-Learning-based methods show superior performance in dynamic obstacle avoidance but are not suitable for long-range navigation and struggle with local minima. In this paper, we propose a Deep-Reinforcement-Learning-based control switch, which has the ability to select between different planning paradigms based solely on sensor data observations. Therefore, we develop an interface to efficiently operate multiple model-based, as well as learning-based local planners and integrate a variety of state-of-the-art planners to be selected by the control switch. Subsequently, we evaluate our approach against each planner individually and found improvements in navigation performance especially for highly dynamic scenarios. Our planner was able to prefer learning-based approaches in situations with a high number of obstacles while relying on the traditional model-based planners in long corridors or empty spaces.