  Generative models are now used to create a variety of high-quality digital
artifacts. Yet their use in designing physical objects has received far less
attention. In this paper, we advocate for the construction toy, LEGO, as a
platform for developing generative models of sequential assembly. We develop a
generative model based on graph-structured neural networks that can learn from
human-built structures and produce visually compelling designs. Our code is
released at: https://github.com/uoguelph-mlrg/GenerativeLEGO.
