  Witnessing the impressive achievements of pre-training techniques on
large-scale data in the field of computer vision and natural language
processing, we wonder whether this idea could be adapted in a grab-and-go
spirit, and mitigate the sample inefficiency problem for visuomotor driving.
Given the highly dynamic and variant nature of the input, the visuomotor
driving task inherently lacks view and translation invariance, and the visual
input contains massive irrelevant information for decision making, resulting in
predominant pre-training approaches from general vision less suitable for the
autonomous driving task. To this end, we propose PPGeo (Policy Pre-training via
Geometric modeling), an intuitive and straightforward fully self-supervised
framework curated for the policy pretraining in visuomotor driving. We aim at
learning policy representations as a powerful abstraction by modeling 3D
geometric scenes on large-scale unlabeled and uncalibrated YouTube driving
videos. The proposed PPGeo is performed in two stages to support effective
self-supervised training. In the first stage, the geometric modeling framework
generates pose and depth predictions simultaneously, with two consecutive
frames as input. In the second stage, the visual encoder learns driving policy
representation by predicting the future ego-motion and optimizing with the
photometric error based on current visual observation only. As such, the
pre-trained visual encoder is equipped with rich driving policy related
representations and thereby competent for multiple visuomotor driving tasks.
Extensive experiments covering a wide span of challenging scenarios have
demonstrated the superiority of our proposed approach, where improvements range
from 2% to even over 100% with very limited data.
