Unsupervised Domain Adaptation for Visual Navigation
This addresses the domain shift issue for embodied navigation agents, enabling practical deployment from simulation to real-world environments, though it appears incremental as it builds on existing domain adaptation techniques.
The paper tackles the problem of transferring visual navigation policies from simulation to the real world by proposing an unsupervised domain adaptation method that translates target domain images to be consistent with learned policy representations, achieving performance improvements over baselines in simulation tasks and enabling real-world transfer.
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.