  To compute robust 2D assembly plans, we present an approach that combines
geometric planning with a deep neural network. We train the network using the
Box2D physics simulator with added stochastic noise to yield robustness
scores--the success probabilities of planned assembly motions. As running a
simulation for every assembly motion is impractical, we train a convolutional
neural network to map assembly operations, given as an image pair of the
subassemblies before and after they are mated, to a robustness score. The
neural network prediction is used within a planner to quickly prune out motions
that are not robust. We demonstrate this approach on two-handed planar
assemblies, where the motions are one-step translations. Results suggest that
the neural network can learn robustness to plan robust sequences an order of
magnitude faster than physics simulation.
