  The in-context learning ability of Transformer models has brought new
possibilities to visual navigation. In this paper, we focus on the video
navigation setting, where an in-context navigation policy needs to be learned
purely from videos in an offline manner, without access to the actual
environment. For this setting, we propose Navigate Only Look Once (NOLO), a
method for learning a navigation policy that possesses the in-context ability
and adapts to new scenes by taking corresponding context videos as input
without finetuning or re-training. To enable learning from videos, we first
propose a pseudo action labeling procedure using optical flow to recover the
action label from egocentric videos. Then, offline reinforcement learning is
applied to learn the navigation policy. Through extensive experiments on
different scenes, we show that our algorithm outperforms baselines by a large
margin, which demonstrates the in-context learning ability of the learned
policy.
