# Planning Assembly Sequence with Graph Transformer

arXiv: https://arxiv.org/abs/2210.05236 · HTML: https://mlsanity.com/paper/2210.05236/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 33 references that are in the map.
- First posted on arXiv 11 Oct 2022.

## Builds on (most cited first, 12 of 12)

- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [A Generalization of Transformer Networks to Graphs](https://mlsanity.com/paper/2012.09699/) (2020)
- [Heterogeneous Graph Transformer](https://mlsanity.com/paper/2003.01332/) (2020)
- [PartNet: A Large-scale Benchmark for Fine-grained and Hierarchical Part-level 3D Object Understanding](https://mlsanity.com/paper/1812.02713/) (2018)
- [Do Transformers Really Perform Bad for Graph Representation?](https://mlsanity.com/paper/2106.05234/) (2021)
- [PQ-NET: A Generative Part Seq2Seq Network for 3D Shapes](https://mlsanity.com/paper/1911.10949/) (2019)
- [Graph Transformer for Graph-to-Sequence Learning](https://mlsanity.com/paper/1911.07470/) (2019)
- [IKEA Furniture Assembly Environment for Long-Horizon Complex Manipulation Tasks](https://mlsanity.com/paper/1911.07246/) (2019)
- [3D Part Assembly Generation with Instance Encoded Transformer](https://mlsanity.com/paper/2207.01779/) (2022)
- [Building LEGO Using Deep Generative Models of Graphs](https://mlsanity.com/paper/2012.11543/) (2020)
- [Combinatorial 3D Shape Generation via Sequential Assembly](https://mlsanity.com/paper/2004.07414/) (2020)
- [Computational LEGO Technic Design](https://mlsanity.com/paper/2007.02245/) (2020)

## Built on by (most cited first, 10 of 10)

- [SPAFormer: Sequential 3D Part Assembly with Transformers](https://mlsanity.com/paper/2403.05874/) (2024)
- [Multi-level Reasoning for Robotic Assembly: From Sequence Inference to Contact Selection](https://mlsanity.com/paper/2312.10571/) (2023)
- [Physics-Aware Combinatorial Assembly Sequence Planning using Data-free Action Masking](https://mlsanity.com/paper/2408.10162/) (2024)
- [ACEvo: Adversarial Co-Evolution of Problem Distributions and Solvers for Combinatorial Optimization](https://mlsanity.com/paper/2506.02594/) (2025)
- [Efficient and Feasible Robotic Assembly Sequence Planning via Graph Representation Learning](https://mlsanity.com/paper/2303.10135/) (2023)
- [Density-based Feasibility Learning with Normalizing Flows for Introspective Robotic Assembly](https://mlsanity.com/paper/2307.01317/) (2023)
- [Rollback-Free Stable Brick Structures Generation](https://mlsanity.com/paper/2605.06947/) (2026)
- [AssemblyComplete: 3D Combinatorial Construction with Deep Reinforcement Learning](https://mlsanity.com/paper/2410.15469/) (2024)
- [Subassembly to Full Assembly: Effective Assembly Sequence Planning through Graph-based Reinforcement Learning](https://mlsanity.com/paper/2409.13620/) (2024)
- [MP-GFormer: A 3D-Geometry-Aware Dynamic Graph Transformer Approach for Machining Process Planning](https://mlsanity.com/paper/2511.11837/) (2025)

## Abstract (arXiv)

Assembly sequence planning (ASP) is the essential process for modern manufacturing, proven to be NP-complete thus its effective and efficient solution has been a challenge for researchers in the field. In this paper, we present a graph-transformer based framework for the ASP problem which is trained and demonstrated on a self-collected ASP database. The ASP database contains a self-collected set of LEGO models. The LEGO model is abstracted to a heterogeneous graph structure after a thorough analysis of the original structure and feature extraction. The ground truth assembly sequence is first generated by brute-force search and then adjusted manually to in line with human rational habits. Based on this self-collected ASP dataset, we propose a heterogeneous graph-transformer framework to learn the latent rules for assembly planning. We evaluated the proposed framework in a series of experiment. The results show that the similarity of the predicted and ground truth sequences can reach 0.44, a medium correlation measured by Kendall's τ. Meanwhile, we compared the different effects of node features and edge features and generated a feasible and reasonable assembly sequence as a benchmark for further research. Our data set and code is available on https://github.com/AIR-DISCOVER/ICRAASP.