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Arranging objects correctly is a key capability for robots which unlocks awide range of useful tasks. A prerequisite for creating successful arrangementsis 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 trainingarrangements offline using an energy-based model, solely from example imageswithout requiring environment interaction or human supervision. Our model isrepresented by a graph neural network which learns object-object relations,using graphs constructed from images. Experiments demonstrate that the learnedcost function can be used to predict poses for missing objects, generalise tonovel objects using semantic features, and can be composed with other costfunctions to satisfy constraints at inference time.

SceneScore: Learning a Cost Function for Object Arrangement