构建工业级装配数据集与模型，实现复杂物体的物理合理装配。
AssemblyBench: Physics-Aware Assembly of Complex Industrial Objects
- 用多模态说明书和3D零件建模联合预测装配顺序与轨迹
- 在2789个工业对象上实现高精度姿态估计与物理可行性轨迹
- 适合机器人装配、智能制造领域研究者参考
从零件组装物体需要理解多模态指令，将其与3D部件关联，并预测每个装配步骤的物理合理6-DoF运动。现有数据集聚焦简化场景，忽视了工业装配中的形状复杂性和装配轨迹。我们提出AssemblyBench，一个包含2,789个工业对象的合成数据集，包含多模态说明书、对应的3D零件模型和装配轨迹。同时提出基于Transformer的模型AssemblyDyno，利用说明书和每个零件的3D形状，联合预测装配顺序与零件装配轨迹。AssemblyDyno在装配位姿估计和轨迹可行性两方面均优于先前方法，后者通过我们设计的物理仿真进行评估。
原文摘要 · Abstract (English)
Assembling objects from parts requires understanding multimodal instructions, linking them to 3D components, and predicting physically plausible 6-DoF motions for each assembly step. Existing datasets focus on simplified scenarios, overlooking shape complexities and assembly trajectories in industrial assemblies. We introduce AssemblyBench, a synthetic dataset of 2,789 industrial objects with multimodal instruction manuals, corresponding 3D part models, and part assembly trajectories. We also propose a transformer-based model, AssemblyDyno, which uses the instructional manual and the 3D shape of each part to jointly predict assembly order and part assembly trajectories. AssemblyDyno outperforms prior works in both assembly pose estimation and trajectory feasibility, where the latter is evaluated by our physics-based simulations.
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