# Deep Reinforcement learning for real autonomous mobile robot navigation in indoor environments

arXiv: https://arxiv.org/abs/2005.13857 · HTML: https://mlsanity.com/paper/2005.13857/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 May 2020.

## Built on by (most cited first, 14 of 14)

- [Learning Robust Autonomous Navigation and Locomotion for Wheeled-Legged Robots](https://mlsanity.com/paper/2405.01792/) (2024)
- [Spatially-Enhanced Recurrent Memory for Long-Range Mapless Navigation via End-to-End Reinforcement Learning](https://mlsanity.com/paper/2506.05997/) (2025)
- [Analysis of Thompson Sampling for Partially Observable Contextual Multi-Armed Bandits](https://mlsanity.com/paper/2110.12175/) (2021)
- [Generalization in Deep Reinforcement Learning for Robotic Navigation by Reward Shaping](https://mlsanity.com/paper/2209.14271/) (2022)
- [Rethinking Adversarial Attacks in Reinforcement Learning from Policy Distribution Perspective](https://mlsanity.com/paper/2501.03562/) (2025)
- [RoboRAN: A Unified Robotics Framework for Reinforcement Learning-Based Autonomous Navigation](https://mlsanity.com/paper/2505.14526/) (2025)
- [An Agency-Transferring Model-Free Policy Enhancement Technique](https://mlsanity.com/paper/2606.09825/) (2026)
- [Integrating Deep Learning and Augmented Reality to Enhance Situational Awareness in Firefighting Environments](https://mlsanity.com/paper/2107.11043/) (2021)
- [Dealing with Sparse Rewards Using Graph Neural Networks](https://mlsanity.com/paper/2203.13424/) (2022)
- [Online No-regret Model-Based Meta RL for Personalized Navigation](https://mlsanity.com/paper/2204.01925/) (2022)
- [Covy: An AI-powered Robot with a Compound Vision System for Detecting Breaches in Social Distancing](https://mlsanity.com/paper/2207.06847/) (2022)
- [A framework for online, stabilizing reinforcement learning](https://mlsanity.com/paper/2207.08730/) (2022)
- [Enhancing team performance with transfer-learning during real-world human-robot collaboration](https://mlsanity.com/paper/2211.13070/) (2022)
- [A Safe Exploration Strategy for Model-free Task Adaptation in Safety-constrained Grid Environments](https://mlsanity.com/paper/2408.00997/) (2024)

## Abstract (arXiv)

Deep Reinforcement Learning has been successfully applied in various computer games [8]. However, it is still rarely used in real-world applications, especially for the navigation and continuous control of real mobile robots [13]. Previous approaches lack safety and robustness and/or need a structured environment. In this paper we present our proof of concept for autonomous self-learning robot navigation in an unknown environment for a real robot without a map or planner. The input for the robot is only the fused data from a 2D laser scanner and a RGB-D camera as well as the orientation to the goal. The map of the environment is unknown. The output actions of an Asynchronous Advantage Actor-Critic network (GA3C) are the linear and angular velocities for the robot. The navigator/controller network is pretrained in a high-speed, parallel, and self-implemented simulation environment to speed up the learning process and then deployed to the real robot. To avoid overfitting, we train relatively small networks, and we add random Gaussian noise to the input laser data. The sensor data fusion with the RGB-D camera allows the robot to navigate in real environments with real 3D obstacle avoidance and without the need to fit the environment to the sensory capabilities of the robot. To further increase the robustness, we train on environments of varying difficulties and run 32 training instances simultaneously. Video: supplementary File / YouTube, Code: GitHub