# Unsupervised Domain Adaptation for Visual Navigation

arXiv: https://arxiv.org/abs/2010.14543 · HTML: https://mlsanity.com/paper/2010.14543/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Oct 2020.

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

- [A Survey on Human-aware Robot Navigation](https://mlsanity.com/paper/2106.11650/) (2021)
- [Success Weighted by Completion Time: A Dynamics-Aware Evaluation Criteria for Embodied Navigation](https://mlsanity.com/paper/2103.08022/) (2021)
- [Interactron: Embodied Adaptive Object Detection](https://mlsanity.com/paper/2202.00660/) (2022)
- [Efficient Policy Adaptation with Contrastive Prompt Ensemble for Embodied Agents](https://mlsanity.com/paper/2412.11484/) (2024)
- [SimCS: Simulation for Domain Incremental Online Continual Segmentation](https://mlsanity.com/paper/2211.16234/) (2022)
- [Self-Supervised Domain Adaptation for Visual Navigation with Global Map Consistency](https://mlsanity.com/paper/2110.07184/) (2021)
- [Moving Forward by Moving Backward: Embedding Action Impact over Action Semantics](https://mlsanity.com/paper/2304.12289/) (2023)
- [Image-based Navigation in Real-World Environments via Multiple Mid-level Representations: Fusion Models, Benchmark and Efficient Evaluation](https://mlsanity.com/paper/2202.01069/) (2022)

## Abstract (arXiv)

Advances in visual navigation methods have led to intelligent embodied navigation agents capable of learning meaningful representations from raw RGB images and perform a wide variety of tasks involving structural and semantic reasoning. However, most learning-based navigation policies are trained and tested in simulation environments. In order for these policies to be practically useful, they need to be transferred to the real-world. In this paper, we propose an unsupervised domain adaptation method for visual navigation. Our method translates the images in the target domain to the source domain such that the translation is consistent with the representations learned by the navigation policy. The proposed method outperforms several baselines across two different navigation tasks in simulation. We further show that our method can be used to transfer the navigation policies learned in simulation to the real world.