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 will be open-sourced.

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与适应性，以应对日益增长的产品定制化需求。实现此类自动化的主要挑战之一在于，如何在复杂度不断增加的装配体中，从指数级增长的潜在序列中高效寻找可行解。此外，机器人系统始终需要进行昂贵的可行性验证。为此，我们提出一种全局图论方法，包括一种用于产品装配体的图表示——装配图，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从图输入中提取有效信息，并逐步预测装配序列。实验表明，基于双臂机器人系统仿真数据，我们的方法能够预测铝型材产品变体中的可行装配序列。我们进一步证明，该方法能够检测不可行装配体，显著减轻错误预测带来的负面影响，从而促进其在实际场景中的快速部署。代码与训练数据将开源。