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Computation of the volume of space required for a robot to execute a sweeping motion from a start to a goal has long been identified as a critical primitive operation in both task and motion planning. However, swept volume computation is particularly challenging for multi-link robots with geometric complexity, e.g., manipulators, due to the non-linear geometry. While earlier work has shown that deep neural networks can approximate the swept volume quantity, a useful parameter in sampling-based planning, general network structures do not lend themselves to outputting geometries. In this paper we train and evaluate the learning of a deep neural network that predicts the swept volume geometry from pairs of robot configurations and outputs discretized voxel grids. We perform this training on a variety of robots from 6 to 16 degrees of freedom. We show that most errors in the prediction of the geometry lie within a …