# Building LEGO Using Deep Generative Models of Graphs

arXiv: https://arxiv.org/abs/2012.11543 · HTML: https://mlsanity.com/paper/2012.11543/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Dec 2020.

## Built on by (most cited first, 14 of 14)

- [Parameter Prediction for Unseen Deep Architectures](https://mlsanity.com/paper/2110.13100/) (2021)
- [On Evaluation Metrics for Graph Generative Models](https://mlsanity.com/paper/2201.09871/) (2022)
- [Generating Physically Stable and Buildable Brick Structures from Text](https://mlsanity.com/paper/2505.05469/) (2025)
- [Blocks Assemble! Learning to Assemble with Large-Scale Structured Reinforcement Learning](https://mlsanity.com/paper/2203.13733/) (2022)
- [Brick-by-Brick: Combinatorial Construction with Deep Reinforcement Learning](https://mlsanity.com/paper/2110.15481/) (2021)
- [Planning Assembly Sequence with Graph Transformer](https://mlsanity.com/paper/2210.05236/) (2022)
- [Break and Make: Interactive Structural Understanding Using LEGO Bricks](https://mlsanity.com/paper/2207.13738/) (2022)
- [BrickNet: Graph-Backed Generative Brick Assembly](https://mlsanity.com/paper/2604.22984/) (2026)
- [Budget-Aware Sequential Brick Assembly with Efficient Constraint Satisfaction](https://mlsanity.com/paper/2210.01021/) (2022)
- [A Lightweight and Transferable Design for Robust LEGO Manipulation](https://mlsanity.com/paper/2309.02354/) (2023)
- [TreeSBA: Tree-Transformer for Self-Supervised Sequential Brick Assembly](https://mlsanity.com/paper/2407.15648/) (2024)
- [Learning to Build by Building Your Own Instructions](https://mlsanity.com/paper/2410.01111/) (2024)
- [GRAN is superior to GraphRNN: node orderings, kernel- and graph embeddings-based metrics for graph generators](https://mlsanity.com/paper/2307.06709/) (2023)
- [Sample-Efficient Post-Training for LEGO Spatial-Physics Reasoning](https://mlsanity.com/paper/2606.07602/) (2026)

## Abstract (arXiv)

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.