  Object-based factorizations provide a useful level of abstraction for
interacting with the world. Building explicit object representations, however,
often requires supervisory signals that are difficult to obtain in practice. We
present a paradigm for learning object-centric representations for physical
scene understanding without direct supervision of object properties. Our model,
Object-Oriented Prediction and Planning (O2P2), jointly learns a perception
function to map from image observations to object representations, a pairwise
physics interaction function to predict the time evolution of a collection of
objects, and a rendering function to map objects back to pixels. For
evaluation, we consider not only the accuracy of the physical predictions of
the model, but also its utility for downstream tasks that require an actionable
representation of intuitive physics. After training our model on an image
prediction task, we can use its learned representations to build block towers
more complicated than those observed during training.
