  A household robot should be able to navigate to target objects without
requiring users to first annotate everything in their home. Most current
approaches to object navigation do not test on real robots and rely solely on
reconstructed scans of houses and their expensively labeled semantic 3D meshes.
In this work, our goal is to build an agent that builds self-supervised models
of the world via exploration, the same as a child might - thus we (1) eschew
the expense of labeled 3D mesh and (2) enable self-supervised in-situ
finetuning in the real world. We identify a strong source of self-supervision
(Location Consistency - LocCon) that can train all components of an ObjectNav
agent, using unannotated simulated houses. Our key insight is that embodied
agents can leverage location consistency as a self-supervision signal -
collecting images from different views/angles and applying contrastive
learning. We show that our agent can perform competitively in the real world
and simulation. Our results also indicate that supervised training with 3D mesh
annotations causes models to learn simulation artifacts, which are not
transferrable to the real world. In contrast, our LocCon shows the most robust
transfer in the real world among the set of models we compare to, and that the
real-world performance of all models can be further improved with
self-supervised LocCon in-situ training.
