让机器人自适应找到家具组装时的最佳支撑点
A3D: Adaptive Affordance Assembly with Dual-Arm Manipulation
- 用点云建模零件交互，自动识别支撑位置
- 根据组装过程反馈动态调整支撑策略
- 可在仿真和真实场景中泛化到50种不同零件
家具组装是机器人面临的关键挑战任务，需双臂精确协同——一臂操作零件，另一臂提供协作支持与稳定。为更高效完成任务，机器人需在长时序组装过程中主动适应支持策略，并泛化至多种零件几何形态。本文提出A3D框架，通过学习自适应可操作性，识别家具零件上的最优支撑与稳定位置。方法采用密集点级几何表示来建模零件间交互模式，实现对多样几何形状的泛化。为应对不断变化的组装状态，引入自适应模块，利用交互反馈动态调整支持策略，基于历史交互信息进行优化。我们构建了一个包含8类家具、50种不同零件的仿真环境，用于双臂协作评估。实验表明，该框架在仿真和真实世界设置中均能有效泛化至多样零件几何与家具类别。
原文摘要 · Abstract (English)
Furniture assembly is a crucial yet challenging task for robots, requiring precise dual-arm coordination where one arm manipulates parts while the other provides collaborative support and stabilization. To accomplish this task more effectively, robots need to actively adapt support strategies throughout the long-horizon assembly process, while also generalizing across diverse part geometries. We propose A3D, a framework which learns adaptive affordances to identify optimal support and stabilization locations on furniture parts. The method employs dense point-level geometric representations to model part interaction patterns, enabling generalization across varied geometries. To handle evolving assembly states, we introduce an adaptive module that uses interaction feedback to dynamically adjust support strategies during assembly based on previous interactions. We establish a simulation environment featuring 50 diverse parts across 8 furniture types, designed for dual-arm collaboration evaluation. Experiments demonstrate that our framework generalizes effectively to diverse part geometries and furniture categories in both simulation and real-world settings.
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