Decision Making in Joint Push-Grasp Action Space for Large-Scale Object Sorting
Decision Making in Joint Push-Grasp Action Space for Large-Scale Object Sorting
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DOI：
10.1109/icra48506.2021.9560782
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发表时间：
2020-10
期刊：
影响因子：
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通讯作者：
Zherong Pan;Kris K. Hauser
中科院分区：
文献类型：
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作者：
Zherong Pan;Kris K. Hauser
我们提出了一个规划器的大规模（联合国）标记的对象排序任务，它使用两种类型的操作动作：头顶抓和平面推。抓取动作在温和的假设下提供了完整性保证，而平面推动是一种同时移动多个物体的加速策略。本文主要做了两个方面的工作：（1）提出了一种双层规划算法。我们的高级计划者基于成本模型在推动和抓取动作之间做出有效的、接近最优的选择。我们的低级规划器计算一步贪婪的推或抓动作。(2)我们提出了一种新的低层次的推动计划，可以找到一步贪婪推动行动在半离散搜索空间。搜索空间的结构使我们能够有效地做出决策。我们表明，对于多达200个对象的排序，我们的规划器可以在10秒内找到接近最佳的行动在台式PC上的计算。
We present a planner for large-scale (un)labeled object sorting tasks, which uses two types of manipulation actions: overhead grasping and planar pushing. The grasping action offers completeness guarantee under mild assumptions, and the planar pushing is an acceleration strategy that moves multiple objects at once. We make two main contributions: (1) We propose a bilevel planning algorithm. Our high-level planner makes efficient, near-optimal choices between pushing and grasping actions based on a cost model. Our low-level planner computes one-step greedy pushing or grasping actions. (2) We propose a novel low-level push planner that can find one-step greedy pushing actions in a semi-discrete search space. The structure of the search space allows us to efficiently make decisions. We show that, for sorting up to 200 objects, our planner can find near-optimal actions within 10 seconds of computation on a desktop PC.