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和适应性，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，如何从复杂度持续提升的装配体中快速找到可行解，而这需要从数量激增的潜在序列中进行筛选。此外，机器人系统始终需要进行成本高昂的可行性校验。为此，我们提出了一种整体性图方法，包括：用于产品装配的图表示——装配图（Assembly Graph），以及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从图输入中提取关键信息，并逐步预测装配序列。实验表明，基于双臂机器人系统仿真采集的数据，我们的方法能够预测铝型材产品变体的可行装配序列。我们进一步证明，该方法可有效检测不可行装配，显著减轻错误预测带来的不利影响，从而加速实际部署进程。相关代码与训练数据将进行开源。