BiAssemble: Learning Collaborative Affordance for Bimanual Geometric Assembly
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arXiv
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866909644945883136 |
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| author | Shen, Yan Wu, Ruihai Ke, Yubin Song, Xinyuan Li, Zeyi Li, Xiaoqi Fan, Hongwei Lu, Haoran dong, Hao |
| author_facet | Shen, Yan Wu, Ruihai Ke, Yubin Song, Xinyuan Li, Zeyi Li, Xiaoqi Fan, Hongwei Lu, Haoran dong, Hao |
| contents | Shape assembly, the process of combining parts into a complete whole, is a crucial robotic skill with broad real-world applications. Among various assembly tasks, geometric assembly--where broken parts are reassembled into their original form (e.g., reconstructing a shattered bowl)--is particularly challenging. This requires the robot to recognize geometric cues for grasping, assembly, and subsequent bimanual collaborative manipulation on varied fragments. In this paper, we exploit the geometric generalization of point-level affordance, learning affordance aware of bimanual collaboration in geometric assembly with long-horizon action sequences. To address the evaluation ambiguity caused by geometry diversity of broken parts, we introduce a real-world benchmark featuring geometric variety and global reproducibility. Extensive experiments demonstrate the superiority of our approach over both previous affordance-based and imitation-based methods. Project page: https://sites.google.com/view/biassembly/. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_06221 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | BiAssemble: Learning Collaborative Affordance for Bimanual Geometric Assembly Shen, Yan Wu, Ruihai Ke, Yubin Song, Xinyuan Li, Zeyi Li, Xiaoqi Fan, Hongwei Lu, Haoran dong, Hao Robotics Machine Learning Shape assembly, the process of combining parts into a complete whole, is a crucial robotic skill with broad real-world applications. Among various assembly tasks, geometric assembly--where broken parts are reassembled into their original form (e.g., reconstructing a shattered bowl)--is particularly challenging. This requires the robot to recognize geometric cues for grasping, assembly, and subsequent bimanual collaborative manipulation on varied fragments. In this paper, we exploit the geometric generalization of point-level affordance, learning affordance aware of bimanual collaboration in geometric assembly with long-horizon action sequences. To address the evaluation ambiguity caused by geometry diversity of broken parts, we introduce a real-world benchmark featuring geometric variety and global reproducibility. Extensive experiments demonstrate the superiority of our approach over both previous affordance-based and imitation-based methods. Project page: https://sites.google.com/view/biassembly/. |
| title | BiAssemble: Learning Collaborative Affordance for Bimanual Geometric Assembly |
| topic | Robotics Machine Learning |
| url | https://arxiv.org/abs/2506.06221 |