Most successes in autonomous robotic assembly have been restricted to single target or category. We propose to investigate general part assembly, the task of creating novel target assemblies with unseen part shapes. To tackle the planning of general part assembly, we present General Part Assembly Transformer (GPAT), a transformer based model architecture that accurately predicts part poses by inferring how each part shape corresponds to the target shape. Our experiments on both 3D CAD models and real-world scans demonstrate GPAT's generalization abilities to novel and diverse target and part shapes. Project website: https://general-part-assembly.github.io/

General Part Assembly Planning