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.

翻译：自主机器人装配领域的大多数成功案例仅限于单目标或单类别。我们提议研究通用零件装配任务，即利用未见过的零件形状创建新颖的目标装配体。作为通用零件装配系统的关键基础步骤，我们致力于确定目标装配体中零件的精确位姿，这一任务被我们称为"重新排列规划"。本文提出通用零件装配变换器（GPAT），这是一种基于变换器架构的模型，通过推断每个零件形状与目标形状的对应关系，精确预测零件位姿。我们在三维CAD模型和真实世界扫描数据上的实验表明，GPAT具备对新颖且多样化的目标与零件形状的泛化能力。