Many everyday objects are difficult to directly grasp (e.g., a flat iPad) or manipulate functionally (e.g., opening the cap of a pen lying on a desk). Such tasks require sequential, asymmetric coordination between two arms, where one arm performs preparatory manipulation that enables the other's goal-directed action - for instance, pushing the iPad to the table's edge before picking it up, or lifting the pen body to allow the other hand to remove its cap. In this work, we introduce Collaborative Preparatory Manipulation, a class of bimanual manipulation tasks that demand understanding object semantics and geometry, anticipating spatial relationships, and planning long-horizon coordinated actions between the two arms. To tackle this challenge, we propose a visual affordance-based framework that first envisions the final goal-directed action and then guides one arm to perform a sequence of preparatory manipulations that facilitate the other arm's subsequent operation. This affordance-centric representation enables anticipatory inter-arm reasoning and coordination, generalizing effectively across various objects spanning diverse categories. Extensive experiments in both simulation and the real world demonstrate that our approach substantially improves task success rates and generalization compared to competitive baselines.

翻译：许多日常物体难以直接抓取（例如平放的iPad）或进行功能性操作（例如打开桌上笔的笔帽）。这类任务要求双臂之间进行顺序性、非对称的协调，其中一只手臂执行预备性操作，为另一只手臂的目标导向动作创造条件——例如，将iPad推到桌边再拾起，或抬起笔身以便另一只手取下笔帽。本文提出"协作性预备操作"这一概念，这是一类需要理解物体语义与几何结构、预判空间关系、并规划双臂间长时程协调动作的双臂操作任务。为应对这一挑战，我们提出基于视觉可操作性的框架：首先构想最终目标导向动作，然后引导一只手臂执行一系列预备操作，以促进另一只手臂的后续操作。这种以可操作性为核心的表示方法实现了双臂间的预期性推理与协调，并能有效泛化至不同类别的多种物体。在仿真与真实环境中的大量实验表明，与竞争性基线方法相比，我们的方法显著提升了任务成功率与泛化能力。