论文标题
使用以对象为中心的操纵计划的有效状态抽象
Efficient State Abstraction using Object-centered Predicates for Manipulation Planning
论文作者
论文摘要
在操纵任务中始终代表相关几何方面的符号描述的定义是一个具有挑战性的问题，在机器人社区中很少关注。这个定义通常是从观察者的角度来完成的，这些观察者的角度仅对一组有限的对象关系和方向进行，这些对象关系和方向只能满足几何约束以在实验室条件下执行实验。这限制了对象配置空间中的操纵操作可能发生的变化，以与该特定外部参考定义兼容的操作，这极大地限制了可能的操作范围。为了应对这些限制，我们提出了一个以对象为中心的表示，该表示允许表征比传统观察者的视角对应的配置空间可能更大的可能更改。基于此表示形式，我们为挑选和放置允许在操作任务中以几何和力一致性生成计划的行动定义了通用计划运营商。这种以对象为中心的描述是使用一种新颖的学习机制直接从对象的姿势和边界框中获得的，该机制允许生成信号 - 符号关系，而无需在每个特定情况下对这些关系进行手工处理。
The definition of symbolic descriptions that consistently represent relevant geometrical aspects in manipulation tasks is a challenging problem that has received little attention in the robotic community. This definition is usually done from an observer perspective of a finite set of object relations and orientations that only satisfy geometrical constraints to execute experiments in laboratory conditions. This restricts the possible changes with manipulation actions in the object configuration space to those compatible with that particular external reference definitions, which greatly limits the spectrum of possible manipulations. To tackle these limitations we propose an object-centered representation that permits characterizing a much wider set of possible changes in configuration spaces than the traditional observer perspective counterpart. Based on this representation, we define universal planning operators for picking and placing actions that permits generating plans with geometric and force consistency in manipulation tasks. This object-centered description is directly obtained from the poses and bounding boxes of objects using a novel learning mechanisms that permits generating signal-symbols relations without the need of handcrafting these relations for each particular scenario.
