  In robotic applications, we often face the challenge of discovering new
objects while having very little or no labelled training data. In this paper we
explore the use of self-supervision provided by a robot traversing an
environment to learn representations of encountered objects. Knowledge of
ego-motion and depth perception enables the agent to effectively associate
multiple object proposals, which serve as training data for learning object
representations from unlabelled images. We demonstrate the utility of this
representation in two ways. First, we can automatically discover objects by
performing clustering in the learned embedding space. Each resulting cluster
contains examples of one instance seen from various viewpoints and scales.
Second, given a small number of labeled images, we can efficiently learn
detectors for these labels. In the few-shot regime, these detectors have a
substantially higher mAP of 0.22 compared to 0.12 of off-the-shelf standard
detectors trained on this limited data. Thus, the proposed self-supervision
results in effective environment specific object discovery and detection at no
or very small human labeling cost.
