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. Secondly, we use GRACE 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.

翻译：自动机器人装配序列规划（RASP）能够显著提升现代制造业的生产效率和弹性，以应对日益增长的个性化产品需求。实现此类自动化的主要挑战之一，在于如何从数量不断增多的潜在装配序列中，为日益复杂的装配体高效地找到可行方案。此外，机器人系统始终需要进行代价高昂的可行性检查。为解决这一问题，我们提出了一种整体性的图方法，包括一种名为装配图（Assembly Graph）的产品装配体图表示，以及一种用于装配序列生成的策略架构——图装配处理网络（GRACE）。其次，我们利用GRACE从图输入中提取有意义的信息，并以逐步的方式预测装配序列。实验表明，我们的方法能够基于双臂机器人系统模拟中收集的数据，预测铝型材不同产品变体的可行装配序列。我们进一步证明，该方法能够检测不可行的装配体，显著减轻错误预测带来的不良影响，从而有助于其近期在现实世界中部署。代码和训练数据可在https://github.com/DLR-RM/GRACE获取。