  There have recently been large advances both in pre-training visual
representations for robotic control and segmenting unknown category objects in
general images. To leverage these for improved robot learning, we propose
$\textbf{POCR}$, a new framework for building pre-trained object-centric
representations for robotic control. Building on theories of "what-where"
representations in psychology and computer vision, we use segmentations from a
pre-trained model to stably locate across timesteps, various entities in the
scene, capturing "where" information. To each such segmented entity, we apply
other pre-trained models that build vector descriptions suitable for robotic
control tasks, thus capturing "what" the entity is. Thus, our pre-trained
object-centric representations for control are constructed by appropriately
combining the outputs of off-the-shelf pre-trained models, with no new
training. On various simulated and real robotic tasks, we show that imitation
policies for robotic manipulators trained on POCR achieve better performance
and systematic generalization than state of the art pre-trained representations
for robotics, as well as prior object-centric representations that are
typically trained from scratch.
