  We study how to learn a policy with compositional generalizability. We
propose a two-stage framework, which refactorizes a high-reward teacher policy
into a generalizable student policy with strong inductive bias. Particularly,
we implement an object-centric GNN-based student policy, whose input objects
are learned from images through self-supervised learning. Empirically, we
evaluate our approach on four difficult tasks that require compositional
generalizability, and achieve superior performance compared to baselines.
