构建新数据集与方法，让机器人学会日常物品配对组装
Two by Two: Learning Multi-Task Pairwise Objects Assembly for Generalizable Robot Manipulation
- 分两步估计物体在3D空间的位姿，利用等变特征处理装配约束
- 在18类真实场景任务中性能领先，1034个实例上表现优异
- 适合研究具身智能、机器人操作与通用装配的学者与工程师
3D装配任务如家具组装和部件嵌合在日常生活中至关重要，是未来家庭机器人的核心能力。现有基准和数据集多聚焦几何碎片或工厂零件，难以应对日常物品交互的复杂性。为此，我们提出2BY2——一个大规模标注的日常成对物品装配数据集，涵盖18类细粒度任务，包括插插座、插花入 vase、面包放入烤面包机等真实场景。2BY2包含1034个实例和517对物品，带有位姿与对称性标注，要求方法同时考虑几何匹配、功能关系与空间约束。基于该数据集，我们提出一种两阶段的SE(3)位姿估计方法，采用等变特征建模装配约束。相比以往形状装配方法，在2BY2所有18项任务上均达到最优性能。机器人实验进一步验证了该方法在复杂3D装配任务中的可靠性与泛化能力。
原文摘要 · Abstract (English)
3D assembly tasks, such as furniture assembly and component fitting, play a crucial role in daily life and represent essential capabilities for future home robots. Existing benchmarks and datasets predominantly focus on assembling geometric fragments or factory parts, which fall short in addressing the complexities of everyday object interactions and assemblies. To bridge this gap, we present 2BY2, a large-scale annotated dataset for daily pairwise objects assembly, covering 18 fine-grained tasks that reflect real-life scenarios, such as plugging into sockets, arranging flowers in vases, and inserting bread into toasters. 2BY2 dataset includes 1,034 instances and 517 pairwise objects with pose and symmetry annotations, requiring approaches that align geometric shapes while accounting for functional and spatial relationships between objects. Leveraging the 2BY2 dataset, we propose a two-step SE(3) pose estimation method with equivariant features for assembly constraints. Compared to previous shape assembly methods, our approach achieves state-of-the-art performance across all 18 tasks in the 2BY2 dataset. Additionally, robot experiments further validate the reliability and generalization ability of our method for complex 3D assembly tasks.
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