  Object-centric world models provide structured representation of the scene
and can be an important backbone in reinforcement learning and planning.
However, existing approaches suffer in partially-observable environments due to
the lack of belief states. In this paper, we propose Structured World Belief, a
model for learning and inference of object-centric belief states. Inferred by
Sequential Monte Carlo (SMC), our belief states provide multiple object-centric
scene hypotheses. To synergize the benefits of SMC particles with object
representations, we also propose a new object-centric dynamics model that
considers the inductive bias of object permanence. This enables tracking of
object states even when they are invisible for a long time. To further
facilitate object tracking in this regime, we allow our model to attend
flexibly to any spatial location in the image which was restricted in previous
models. In experiments, we show that object-centric belief provides a more
accurate and robust performance for filtering and generation. Furthermore, we
show the efficacy of structured world belief in improving the performance of
reinforcement learning, planning and supervised reasoning.
