  Visual learning often occurs in a specific context, where an agent acquires
skills through exploration and tracking of its location in a consistent
environment. The historical spatial context of the agent provides a similarity
signal for self-supervised contrastive learning. We present a unique approach,
termed Environmental Spatial Similarity (ESS), that complements existing
contrastive learning methods. Using images from simulated, photorealistic
environments as an experimental setting, we demonstrate that ESS outperforms
traditional instance discrimination approaches. Moreover, sampling additional
data from the same environment substantially improves accuracy and provides new
augmentations. ESS allows remarkable proficiency in room classification and
spatial prediction tasks, especially in unfamiliar environments. This learning
paradigm has the potential to enable rapid visual learning in agents operating
in new environments with unique visual characteristics. Potentially
transformative applications span from robotics to space exploration. Our proof
of concept demonstrates improved efficiency over methods that rely on
extensive, disconnected datasets.
