We present a framework for deformable object manipulation that interleaves planning and control, enabling complex manipulation tasks without relying on high-fidelity modeling or simulation. The key question we address is when should we use planning and when should we use control to achieve the task? Planners are designed to find paths through complex configuration spaces, but for highly underactuated systems, such as deformable objects, achieving a specific configuration is very difficult even with high-fidelity models. Conversely, controllers can be designed to achieve specific configurations, but they can be trapped in undesirable local minima owing to obstacles. Our approach consists of three components: (1) a global motion planner to generate gross motion of the deformable object; (2) a local controller for refinement of the configuration of the deformable object; and (3) a novel deadlock prediction algorithm to determine when to use planning versus control. By separating planning from control we are able to use different representations of the deformable object, reducing overall complexity and enabling efficient computation of motion. We provide a detailed proof of probabilistic completeness for our planner, which is valid despite the fact that our system is underactuated and we do not have a steering function. We then demonstrate that our framework is able to successfully perform several manipulation tasks with rope and cloth in simulation, which cannot be performed using either our controller or planner alone. These experiments suggest that our planner can generate paths efficiently, taking under a second on average to find a feasible path in three out of four scenarios. We also show that our framework is effective on a 16-degree-of-freedom physical robot, where reachability and dual-arm constraints make the planning more difficult.

中文翻译：

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通过交织预测、规划和控制来操纵可变形对象

我们提出了一个用于可变形对象操作的框架，该框架将规划和控制交织在一起，在不依赖高保真建模或模拟的情况下实现复杂的操作任务。我们要解决的关键问题是什么时候应该使用计划，什么时候应该使用控制来完成任务？规划器旨在通过复杂的配置空间找到路径，但对于高度欠驱动的系统，例如可变形物体，即使使用高保真模型也很难实现特定配置。相反，控制器可以设计为实现特定配置，但由于障碍物，它们可能会陷入不良的局部最小值。我们的方法由三个部分组成：（1）一个全局运动规划器，用于生成可变形对象的粗略运动；(2) 用于细化可变形物体配置的局部控制器；(3) 一种新颖的死锁预测算法，用于确定何时使用计划与控制。通过将规划与控制分开，我们能够使用可变形对象的不同表示，从而降低整体复杂性并实现有效的运动计算。我们为我们的规划器提供了概率完整性的详细证明，尽管我们的系统驱动不足并且我们没有转向功能，但它仍然有效。然后，我们证明我们的框架能够在模拟中成功地使用绳索和布料执行多项操作任务，而这些任务单独使用我们的控制器或规划器是无法执行的。这些实验表明我们的规划器可以有效地生成路径，在四种情况中的三种情况下，平均需要不到一秒钟的时间来找到可行的路径。我们还表明，我们的框架在 16 自由度物理机器人上是有效的，其中可达性和双臂约束使规划更加困难。