# Visual Navigation in Real-World Indoor Environments Using End-to-End Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/2010.10903 · HTML: https://mlsanity.com/paper/2010.10903/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Oct 2020.

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

- [One-4-All: Neural Potential Fields for Embodied Navigation](https://mlsanity.com/paper/2303.04011/) (2023)
- [Train a Real-world Local Path Planner in One Hour via Partially Decoupled Reinforcement Learning and Vectorized Diversity](https://mlsanity.com/paper/2305.04180/) (2023)
- [Using Implicit Behavior Cloning and Dynamic Movement Primitive to Facilitate Reinforcement Learning for Robot Motion Planning](https://mlsanity.com/paper/2307.16062/) (2023)
- [Language-Based Augmentation to Address Shortcut Learning in Object Goal Navigation](https://mlsanity.com/paper/2402.05090/) (2024)
- [SkillTree: Explainable Skill-Based Deep Reinforcement Learning for Long-Horizon Control Tasks](https://mlsanity.com/paper/2411.12173/) (2024)
- [Enhancing Robot Navigation Policies with Task-Specific Uncertainty Managements](https://mlsanity.com/paper/2505.13837/) (2025)
- [Stronger Generalization Guarantees for Robot Learning by Combining Generative Models and Real-World Data](https://mlsanity.com/paper/2111.08761/) (2021)
- [Mobile Robots through Task-Based Human Instructions using Incremental Curriculum Learning](https://mlsanity.com/paper/2412.19159/) (2024)
- [DUViN: Diffusion-Based Underwater Visual Navigation via Knowledge-Transferred Depth Features](https://mlsanity.com/paper/2509.02983/) (2025)
- [Rectified Schr"odinger Bridge Matching for Few-Step Visual Navigation](https://mlsanity.com/paper/2604.05673/) (2026)
- [Covy: An AI-powered Robot with a Compound Vision System for Detecting Breaches in Social Distancing](https://mlsanity.com/paper/2207.06847/) (2022)
- [UAS Navigation in the Real World Using Visual Observation](https://mlsanity.com/paper/2208.12125/) (2022)
- [Monocular Obstacle Avoidance Based on Inverse PPO for Fixed-wing UAVs](https://mlsanity.com/paper/2411.18009/) (2024)
- [ColorDynamic: Generalizable, Scalable, Real-time, End-to-end Local Planner for Unstructured and Dynamic Environments](https://mlsanity.com/paper/2502.19892/) (2025)

## Abstract (arXiv)

Visual navigation is essential for many applications in robotics, from manipulation, through mobile robotics to automated driving. Deep reinforcement learning (DRL) provides an elegant map-free approach integrating image processing, localization, and planning in one module, which can be trained and therefore optimized for a given environment. However, to date, DRL-based visual navigation was validated exclusively in simulation, where the simulator provides information that is not available in the real world, e.g., the robot's position or image segmentation masks. This precludes the use of the learned policy on a real robot. Therefore, we propose a novel approach that enables a direct deployment of the trained policy on real robots. We have designed visual auxiliary tasks, a tailored reward scheme, and a new powerful simulator to facilitate domain randomization. The policy is fine-tuned on images collected from real-world environments. We have evaluated the method on a mobile robot in a real office environment. The training took ~30 hours on a single GPU. In 30 navigation experiments, the robot reached a 0.3-meter neighborhood of the goal in more than 86.7% of cases. This result makes the proposed method directly applicable to tasks like mobile manipulation.