Abstract Humans have a remarkable ability to use physical commonsense and predict the effect of collisions. But do they understand the underlying factors? Can they predict if the underlying factors have changed?
Interestingly, in most cases humans can predict the effects of similar
collisions with different conditions such as changes in mass, friction, etc.
It is postulated this is primarily because we learn to model physics with
meaningful latent variables. This does not imply we can estimate the
precise values of these meaningful variables (estimate exact values of mass
or friction). Inspired by this observation, we propose an interpretable
intuitive physics model where specific dimensions in the bottleneck layers
correspond to different physical properties. In order to demonstrate that
our system models these underlying physical properties, we train our
model on collisions of different shapes (cube, cone, cylinder, spheres etc.)
and test on collisions of unseen combinations of shapes. Furthermore,
we demonstrate our model generalizes well even when similar scenes are
simulated with different underlying properties