  We study the problem of multimodal physical scene understanding, where an
embodied agent needs to find fallen objects by inferring object properties,
direction, and distance of an impact sound source. Previous works adopt
feed-forward neural networks to directly regress the variables from sound,
leading to poor generalization and domain adaptation issues. In this paper, we
illustrate that learning a disentangled model of acoustic formation, referred
to as disentangled acoustic field (DAF), to capture the sound generation and
propagation process, enables the embodied agent to construct a spatial
uncertainty map over where the objects may have fallen. We demonstrate that our
analysis-by-synthesis framework can jointly infer sound properties by
explicitly decomposing and factorizing the latent space of the disentangled
model. We further show that the spatial uncertainty map can significantly
improve the success rate for the localization of fallen objects by proposing
multiple plausible exploration locations.
