# Robot Navigation with Map-Based Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/2002.04349 · HTML: https://mlsanity.com/paper/2002.04349/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Feb 2020.

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

- [Security Considerations in AI-Robotics: A Survey of Current Methods, Challenges, and Opportunities](https://mlsanity.com/paper/2310.08565/) (2023)
- [Mobile Robot Navigation Using Hand-Drawn Maps: A Vision Language Model Approach](https://mlsanity.com/paper/2502.00114/) (2025)
- [Goal-constrained Sparse Reinforcement Learning for End-to-End Driving](https://mlsanity.com/paper/2103.09189/) (2021)
- [Using Deep Reinforcement Learning with Automatic Curriculum Learning for Mapless Navigation in Intralogistics](https://mlsanity.com/paper/2202.11512/) (2022)
- [SGN-CIRL: Scene Graph-based Navigation with Curriculum, Imitation, and Reinforcement Learning](https://mlsanity.com/paper/2506.04505/) (2025)
- [Socially Pertinent Robots in Gerontological Healthcare](https://mlsanity.com/paper/2404.07560/) (2024)
- [Are Learning-Based Approaches Ready for Real-World Indoor Navigation? A Case for Imitation Learning](https://mlsanity.com/paper/2507.04086/) (2025)
- [Beyond Pixels: Introducing Geometric-Semantic World Priors for Video-based Embodied Models via Spatio-temporal Alignment](https://mlsanity.com/paper/2509.00210/) (2025)
- [LASAR: Towards Spatio-temporal Reasoning with Latent Cognitive Map](https://mlsanity.com/paper/2605.16899/) (2026)

## Abstract (arXiv)

This paper proposes an end-to-end deep reinforcement learning approach for mobile robot navigation with dynamic obstacles avoidance. Using experience collected in a simulation environment, a convolutional neural network (CNN) is trained to predict proper steering actions of a robot from its egocentric local occupancy maps, which accommodate various sensors and fusion algorithms. The trained neural network is then transferred and executed on a real-world mobile robot to guide its local path planning. The new approach is evaluated both qualitatively and quantitatively in simulation and real-world robot experiments. The results show that the map-based end-to-end navigation model is easy to be deployed to a robotic platform, robust to sensor noise and outperforms other existing DRL-based models in many indicators.