  2D top-down maps are commonly used for the navigation and exploration of
mobile robots through unknown areas. Typically, the robot builds the navigation
maps incrementally from local observations using onboard sensors. Recent works
have shown that predicting the structural patterns in the environment through
learning-based approaches can greatly enhance task efficiency. While many such
works build task-specific networks using limited datasets, we show that the
existing foundational vision networks can accomplish the same without any
fine-tuning. Specifically, we use Masked Autoencoders, pre-trained on street
images, to present novel applications for field-of-view expansion, single-agent
topological exploration, and multi-agent exploration for indoor mapping, across
different input modalities. Our work motivates the use of foundational vision
models for generalized structure prediction-driven applications, especially in
the dearth of training data. For more qualitative results see
https://raaslab.org/projects/MIM4Robots.

Robotics;Computer Vision and Pattern Recognition