# Efficient and Feasible Robotic Assembly Sequence Planning via Graph Representation Learning

arXiv: https://arxiv.org/abs/2303.10135 · HTML: https://mlsanity.com/paper/2303.10135/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 39 references that are in the map.
- First posted on arXiv 17 Mar 2023.

## Builds on (most cited first, 14 of 14)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Neural Message Passing for Quantum Chemistry](https://mlsanity.com/paper/1704.01212/) (2017)
- [Fast Graph Representation Learning with PyTorch Geometric](https://mlsanity.com/paper/1903.02428/) (2019)
- [Instance Normalization: The Missing Ingredient for Fast Stylization](https://mlsanity.com/paper/1607.08022/) (2016)
- [How Attentive are Graph Attention Networks?](https://mlsanity.com/paper/2105.14491/) (2021)
- [Heterogeneous Graph Attention Network](https://mlsanity.com/paper/1903.07293/) (2019)
- [Hierarchical Planning for Long-Horizon Manipulation with Geometric and Symbolic Scene Graphs](https://mlsanity.com/paper/2012.07277/) (2020)
- [Towards Practical Multi-Object Manipulation using Relational Reinforcement Learning](https://mlsanity.com/paper/1912.11032/) (2019)
- [Structured agents for physical construction](https://mlsanity.com/paper/1904.03177/) (2019)
- [Efficient and Interpretable Robot Manipulation with Graph Neural Networks](https://mlsanity.com/paper/2102.13177/) (2021)
- [Object-centric Forward Modeling for Model Predictive Control](https://mlsanity.com/paper/1910.03568/) (2019)
- [Planning Assembly Sequence with Graph Transformer](https://mlsanity.com/paper/2210.05236/) (2022)
- [Introspective Robot Perception using Smoothed Predictions from Bayesian Neural Networks](https://mlsanity.com/paper/2109.12869/) (2021)

## Built on by (most cited first, 3 of 3)

- [Physics-Aware Combinatorial Assembly Sequence Planning using Data-free Action Masking](https://mlsanity.com/paper/2408.10162/) (2024)
- [Density-based Feasibility Learning with Normalizing Flows for Introspective Robotic Assembly](https://mlsanity.com/paper/2307.01317/) (2023)
- [Subassembly to Full Assembly: Effective Assembly Sequence Planning through Graph-based Reinforcement Learning](https://mlsanity.com/paper/2409.13620/) (2024)

## Abstract (arXiv)

Automatic Robotic Assembly Sequence Planning (RASP) can significantly improve productivity and resilience in modern manufacturing along with the growing need for greater product customization. One of the main challenges in realizing such automation resides in efficiently finding solutions from a growing number of potential sequences for increasingly complex assemblies. Besides, costly feasibility checks are always required for the robotic system. To address this, we propose a holistic graphical approach including a graph representation called Assembly Graph for product assemblies and a policy architecture, Graph Assembly Processing Network, dubbed GRACE for assembly sequence generation. With GRACE, we are able to extract meaningful information from the graph input and predict assembly sequences in a step-by-step manner. In experiments, we show that our approach can predict feasible assembly sequences across product variants of aluminum profiles based on data collected in simulation of a dual-armed robotic system. We further demonstrate that our method is capable of detecting infeasible assemblies, substantially alleviating the undesirable impacts from false predictions, and hence facilitating real-world deployment soon. Code and training data are available at https://github.com/DLR-RM/GRACE.