  To determine if a skill can be executed in any given environment, a robot
needs to learn the preconditions for the skill. As robots begin to operate in
dynamic and unstructured environments, precondition models will need to
generalize to variable number of objects with different shapes and sizes. In
this work, we focus on learning precondition models for manipulation skills in
unconstrained environments. Our work is motivated by the intuition that many
complex manipulation tasks, with multiple objects, can be simplified by
focusing on less complex pairwise object relations. We propose an
object-relation model that learns continuous representations for these pairwise
object relations. Our object-relation model is trained completely in
simulation, and once learned, is used by a separate precondition model to
predict skill preconditions for real world tasks. We evaluate our precondition
model on $3$ different manipulation tasks: sweeping, cutting, and unstacking.
We show that our approach leads to significant improvements in predicting
preconditions for all 3 tasks, across objects of different shapes and sizes.
