  Robots that arrange household objects should do so according to the user's
preferences, which are inherently subjective and difficult to model. We present
NeatNet: a novel Variational Autoencoder architecture using Graph Neural
Network layers, which can extract a low-dimensional latent preference vector
from a user by observing how they arrange scenes. Given any set of objects,
this vector can then be used to generate an arrangement which is tailored to
that user's spatial preferences, with word embeddings used for generalisation
to new objects. We develop a tidying simulator to gather rearrangement examples
from 75 users, and demonstrate empirically that our method consistently
produces neat and personalised arrangements across a variety of rearrangement
scenarios.
