視覚ナビゲーションのための教師なしドメイン適応
Unsupervised Domain Adaptation for Visual Navigation
シミュレーションで学習した視覚ナビゲーション方策を実世界に転移するため、ナビゲーション方策の表現と整合するよう目標ドメイン画像を源ドメインへ変換する教師なしドメイン適応手法を提案した。
著者: Shangda Li, Devendra Singh Chaplot, Yao-Hung Hubert Tsai, Yue Wu, Louis-Philippe Morency, Ruslan Salakhutdinov
分類: cs.LG, cs.CV, cs.RO
原文アブストラクト
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.
