SceneScore: Learning a Cost Function for Object Arrangement
Ivan Kapelyukh Edward Johns
Arranging objects correctly is a key capability for robots which unlocks a
wide range of useful tasks. A prerequisite for creating successful arrangements
is the ability to evaluate the desirability of a given arrangement. Our method
"SceneScore" learns a cost function for arrangements, such that desirable,
human-like arrangements have a low cost. We learn the distribution of training
arrangements offline using an energy-based model, solely from example images
without requiring environment interaction or human supervision. Our model is
represented by a graph neural network which learns object-object relations,
using graphs constructed from images. Experiments demonstrate that the learned
cost function can be used to predict poses for missing objects, generalise to
novel objects using semantic features, and can be composed with other cost
functions to satisfy constraints at inference time.