  When an object detector is deployed in a novel setting it often experiences a
drop in performance. This paper studies how an embodied agent can automatically
fine-tune a pre-existing object detector while exploring and acquiring images
in a new environment without relying on human intervention, i.e., a fully
self-supervised approach. In our setting, an agent initially learns to explore
the environment using a pre-trained off-the-shelf detector to locate objects
and associate pseudo-labels. By assuming that pseudo-labels for the same object
must be consistent across different views, we learn the exploration policy Look
Around to mine hard samples, and we devise a novel mechanism called
Disagreement Reconciliation for producing refined pseudo-labels from the
consensus among observations. We implement a unified benchmark of the current
state-of-the-art and compare our approach with pre-existing exploration
policies and perception mechanisms. Our method is shown to outperform existing
approaches, improving the object detector by 6.2% in a simulated scenario, a
3.59% advancement over other state-of-the-art methods, and by 9.97% in the real
robotic test without relying on ground-truth. Code for the proposed approach
and baselines are available at
https://iit-pavis.github.io/Look_Around_And_Learn/.
