  Modular meta-learning is a new framework that generalizes to unseen datasets
by combining a small set of neural modules in different ways. In this work we
propose abstract graph networks: using graphs as abstractions of a system's
subparts without a fixed assignment of nodes to system subparts, for which we
would need supervision. We combine this idea with modular meta-learning to get
a flexible framework with combinatorial generalization to new tasks built in.
We then use it to model the pushing of arbitrarily shaped objects from little
or no training data.
