arxiv:2012.11543

Building LEGO Using Deep Generative Models of Graphs

Published on Dec 21, 2020

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Abstract

Graph-structured neural networks are employed to develop generative models for sequential assembly design, learning from human-built structures to create visually compelling designs.

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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.

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