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Automatic Robotic Assembly Sequence Planning (RASP) can significantly improveproductivity and resilience in modern manufacturing along with the growing needfor greater product customization. One of the main challenges in realizing suchautomation resides in efficiently finding solutions from a growing number ofpotential sequences for increasingly complex assemblies. Besides, costlyfeasibility checks are always required for the robotic system. To address this,we propose a holistic graphical approach including a graph representationcalled Assembly Graph for product assemblies and a policy architecture, GraphAssembly Processing Network, dubbed GRACE for assembly sequence generation.With GRACE, we are able to extract meaningful information from the graph inputand predict assembly sequences in a step-by-step manner. In experiments, weshow that our approach can predict feasible assembly sequences across productvariants of aluminum profiles based on data collected in simulation of adual-armed robotic system. We further demonstrate that our method is capable ofdetecting infeasible assemblies, substantially alleviating the undesirableimpacts from false predictions, and hence facilitating real-world deploymentsoon. Code and training data are available at https://github.com/DLR-RM/GRACE.

Efficient and Feasible Robotic Assembly Sequence Planning via Graph Representation Learning