AssemblyBench: Physics-Aware Assembly of Complex Industrial Objects
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866910214393954304 |
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| author | Li, Danrui Zhang, Jiahao Egger, Bernhard Chatterjee, Moitreya Lohit, Suhas Marks, Tim K. Cherian, Anoop |
| author_facet | Li, Danrui Zhang, Jiahao Egger, Bernhard Chatterjee, Moitreya Lohit, Suhas Marks, Tim K. Cherian, Anoop |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_12845 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | AssemblyBench: Physics-Aware Assembly of Complex Industrial Objects Li, Danrui Zhang, Jiahao Egger, Bernhard Chatterjee, Moitreya Lohit, Suhas Marks, Tim K. Cherian, Anoop Computer Vision and Pattern Recognition Artificial Intelligence 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. |
| title | AssemblyBench: Physics-Aware Assembly of Complex Industrial Objects |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2605.12845 |