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. As a fundamental
step to a general part assembly system, we tackle the task of determining the
precise poses of the parts in the target assembly, which we we term
``rearrangement planning''. 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.Comment: Project website: https://general-part-assembly.github.io