  Humans excel at efficiently navigating through crowds without collision by
focusing on specific visual regions relevant to navigation. However, most
robotic visual navigation methods rely on deep learning models pre-trained on
vision tasks, which prioritize salient objects -- not necessarily relevant to
navigation and potentially misleading. Alternative approaches train specialized
navigation models from scratch, requiring significant computation. On the other
hand, self-supervised learning has revolutionized computer vision and natural
language processing, but its application to robotic navigation remains
underexplored due to the difficulty of defining effective self-supervision
signals. Motivated by these observations, in this work, we propose a
Self-Supervised Vision-Action Model for Visual Navigation Pre-Training (VANP).
Instead of detecting salient objects that are beneficial for tasks such as
classification or detection, VANP learns to focus only on specific visual
regions that are relevant to the navigation task. To achieve this, VANP uses a
history of visual observations, future actions, and a goal image for
self-supervision, and embeds them using two small Transformer Encoders. Then,
VANP maximizes the information between the embeddings by using a mutual
information maximization objective function. We demonstrate that most
VANP-extracted features match with human navigation intuition. VANP achieves
comparable performance as models learned end-to-end with half the training time
and models trained on a large-scale, fully supervised dataset, i.e., ImageNet,
with only 0.08% data.
