  Robots have the capability to collect large amounts of data autonomously by
interacting with objects in the world. However, it is often not obvious
\emph{how} to learning from autonomously collected data without human-labeled
supervision. In this work we learn pixel-wise object representations from
unsupervised pick and place data that generalize to new objects. We introduce a
novel framework for using these representations in order to predict where to
pick and where to place in order to match a goal image. Finally, we demonstrate
the utility of our approach in a simulated grasping environment.
