Fast High-Quality Tabletop Rearrangement in Bounded Workspace
Fast High-Quality Tabletop Rearrangement in Bounded Workspace
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DOI：
10.1109/icra46639.2022.9812367
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发表时间：
2021-10
期刊：
影响因子：
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通讯作者：
Kai Gao;D. Lau;Baichuan Huang;Kostas E. Bekris;Jingjin Yu
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文献类型：
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作者：
Kai Gao;D. Lau;Baichuan Huang;Kostas E. Bekris;Jingjin Yu
在本文中，我们研究的问题，重新安排许多对象在一个杂乱的设置使用上手掌握在桌面上。有效的解决方案，捕捉我们每天解决的常见任务，对于实现真正的智能机器人操作至关重要。在给定的实例中，对象可能需要被放置在临时位置（“缓冲区”）以完成重新布置，但是在杂乱的环境中分配这些缓冲区位置可能是非常具有挑战性的。为了解决这一挑战，首先开发了两步基线规划器，该基线规划器基于由对象的起始和目标姿态引起的固有组合约束生成原始规划，然后在原始规划的帮助下选择缓冲器位置。然后，我们采用了“懒惰”的规划，在树搜索框架，这是进一步加快了适应一种新的预处理例程。仿真实验表明，我们的方法可以快速生成高质量的解决方案，并在解决大规模的情况下比现有的国家的最先进的方法更强大。来源：github.com/arc-l/TRLB
In this paper, we examine the problem of rearranging many objects on a tabletop in a cluttered setting using overhand grasps. Efficient solutions for the problem, which capture a common task that we solve on a daily basis, are essential in enabling truly intelligent robotic manipulation. In a given instance, objects may need to be placed at temporary positions (“buffers”) to complete the rearrangement, but allocating these buffer locations can be highly challenging in a cluttered environment. To tackle the challenge, a two-step baseline planner is first developed, which generates a primitive plan based on inherent combinatorial constraints induced by start and goal poses of the objects and then selects buffer locations assisted by the primitive plan. We then employ the “lazy” planner in a tree search framework which is further sped up by adapting a novel preprocessing routine. Simulation experiments show our methods can quickly generate high-quality solutions and are more robust in solving large-scale instances than existing state-of-the-art approaches. source: github.com/arc-l/TRLB