  While learning models of intuitive physics is an increasingly active area of
research, current approaches still fall short of natural intelligences in one
important regard: they require external supervision, such as explicit access to
physical states, at training and sometimes even at test times. Some authors
have relaxed such requirements by supplementing the model with an handcrafted
physical simulator. Still, the resulting methods are unable to automatically
learn new complex environments and to understand physical interactions within
them. In this work, we demonstrated for the first time learning such predictors
directly from raw visual observations and without relying on simulators. We do
so in two steps: first, we learn to track mechanically-salient objects in
videos using causality and equivariance, two unsupervised learning principles
that do not require auto-encoding. Second, we demonstrate that the extracted
positions are sufficient to successfully train visual motion predictors that
can take the underlying environment into account. We validate our predictors on
synthetic datasets; then, we introduce a new dataset, ROLL4REAL, consisting of
real objects rolling on complex terrains (pool table, elliptical bowl, and
random height-field). We show that in all such cases it is possible to learn
reliable extrapolators of the object trajectories from raw videos alone,
without any form of external supervision and with no more prior knowledge than
the choice of a convolutional neural network architecture.
