  The ability to discover abstract physical concepts and understand how they
work in the world through observing lies at the core of human intelligence. The
acquisition of this ability is based on compositionally perceiving the
environment in terms of objects and relations in an unsupervised manner. Recent
approaches learn object-centric representations and capture visually observable
concepts of objects, e.g., shape, size, and location. In this paper, we take a
step forward and try to discover and represent intrinsic physical concepts such
as mass and charge. We introduce the PHYsical Concepts Inference NEtwork
(PHYCINE), a system that infers physical concepts in different abstract levels
without supervision. The key insights underlining PHYCINE are two-fold,
commonsense knowledge emerges with prediction, and physical concepts of
different abstract levels should be reasoned in a bottom-up fashion. Empirical
evaluation demonstrates that variables inferred by our system work in
accordance with the properties of the corresponding physical concepts. We also
show that object representations containing the discovered physical concepts
variables could help achieve better performance in causal reasoning tasks,
i.e., ComPhy.
