  Humans learn a predictive model of the world and use this model to reason
about future events and the consequences of actions. In contrast to most
machine predictors, we exhibit an impressive ability to generalize to unseen
scenarios and reason intelligently in these settings. One important aspect of
this ability is physical intuition(Lake et al., 2016). In this work, we explore
the potential of unsupervised learning to find features that promote better
generalization to settings outside the supervised training distribution. Our
task is predicting the stability of towers of square blocks. We demonstrate
that an unsupervised model, trained to predict future frames of a video
sequence of stable and unstable block configurations, can yield features that
support extrapolating stability prediction to blocks configurations outside the
training set distribution
