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.

翻译：家具装配是机器人面临的关键且具有挑战性的任务，需要精确的双臂协调：一只手臂操作零件，另一只手臂提供协作支撑与稳定。为更有效地完成该任务，机器人在长时序装配过程中需主动适应支撑策略，同时泛化至多样化的零件几何形态。本文提出A3D框架，通过学习自适应功能感知识别家具零件上的最优支撑与稳定位置。该方法采用稠密点级几何表征建模零件交互模式，实现对不同几何形态的泛化能力。为处理动态演化的装配状态，我们引入自适应模块，利用交互反馈在装配过程中基于历史交互动态调整支撑策略。我们构建了包含8类家具共50种差异化零件的仿真环境，专为双臂协作评估设计。实验表明，该框架在仿真与真实场景中均能有效泛化至不同几何形态的零件及家具类别。