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Unsupervised Domain Adaptation for Visual Navigation
[article]
2020
arXiv
pre-print
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
arXiv:2010.14543v2
fatcat:evtvmq4wg5gyzoldvwwaupjxde