  Perceptual understanding of the scene and the relationship between its
different components is important for successful completion of robotic tasks.
Representation learning has been shown to be a powerful technique for this, but
most of the current methodologies learn task specific representations that do
not necessarily transfer well to other tasks. Furthermore, representations
learned by supervised methods require large labeled datasets for each task that
are expensive to collect in the real world. Using self-supervised learning to
obtain representations from unlabeled data can mitigate this problem. However,
current self-supervised representation learning methods are mostly object
agnostic, and we demonstrate that the resulting representations are
insufficient for general purpose robotics tasks as they fail to capture the
complexity of scenes with many components. In this paper, we explore the
effectiveness of using object-aware representation learning techniques for
robotic tasks. Our self-supervised representations are learned by observing the
agent freely interacting with different parts of the environment and is queried
in two different settings: (i) policy learning and (ii) object location
prediction. We show that our model learns control policies in a
sample-efficient manner and outperforms state-of-the-art object agnostic
techniques as well as methods trained on raw RGB images. Our results show a 20
percent increase in performance in low data regimes (1000 trajectories) in
policy training using implicit behavioral cloning (IBC). Furthermore, our
method outperforms the baselines for the task of object localization in
multi-object scenes.
