BiAssemble：学习面向双臂几何装配的协同可供性
BiAssemble: Learning Collaborative Affordance for Bimanual Geometric Assembly
AI总结：
本文提出BiAssemble，通过学习点级双臂协同可供性完成长时序几何装配，并构建真实世界可复现基准，实验证明其优于现有基于可供性和模仿的方法。
摘要：
形状装配是将多个部件组合成一个完整整体的过程，也是一项具有广泛现实应用的关键机器人技能。在各类装配任务中，几何装配——即将破碎部件重新装配成其原始形态（例如重建一个摔碎的碗）——尤其具有挑战性。这要求机器人识别用于抓取、装配以及随后对不同碎片进行双臂协同操作的几何线索。本文利用点级可供性的几何泛化能力，在具有长时序动作序列的几何装配中学习感知双臂协作的可供性。为解决破碎部件几何多样性导致的评估歧义，我们引入了一个兼具几何多样性和全局可复现性的真实世界基准。大量实验表明，我们的方法优于以往基于可供性和基于模仿的方法。项目页面：https://sites.google.com/view/biassembly/。
英文摘要：
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/.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
