让机器人学会双手协作拼合破碎物体，提升复杂几何装配能力
BiAssemble: Learning Collaborative Affordance for Bimanual Geometric Assembly
- 基于点级交互线索，学习双臂协作的装配潜能
- 在真实多变碎片场景中实现更高装配成功率与动作连贯性
- 适合研究具身智能、双臂协作与复杂装配任务的学者
形状装配是将零件组合成完整整体的关键机器人技能，具有广泛现实应用。其中几何装配——将破碎部件重新拼回原形（如修复碎碗）——尤为困难，要求机器人识别抓取、装配及后续双臂协同操作的几何线索。本文利用点级潜能的几何泛化特性，学习具备双臂协作意识的几何装配策略，支持长时序动作序列。为解决因碎片几何多样性带来的评估模糊问题，我们构建了一个真实世界基准，涵盖几何多样性与全局可复现性。大量实验证明，该方法在性能上显著优于以往基于潜能和模仿学习的方法。
原文摘要 · Abstract (English)
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/.
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