POMDP approach to robotized clothes separation
POMDP approach to robotized clothes separation
复制标题
自动化衣物分类的 POMDP 方法
DOI：
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发表时间：
2012
期刊：
影响因子：
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通讯作者：
C. Torras
中科院分区：
文献类型：
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作者：
Pol Monso;G. Alenyà;C. Torras
用机器人操纵的刚性对象主要取决于精确的，昂贵的模型和确定性序列。鉴于准确建模可变形物体的复杂性，它们的操作似乎要求采用一种相当不同的方法。本文提出了一个基于可观察到的马尔可夫决策过程（POMDP）的概率计划者，该计划旨在降低可变形对象排序的固有不确定性。结果表明，只要事先收集了对这两个忠实的统计数据，就可以完成一小部分不可靠的动作和不准确的看法来完成任务。计划者已在带有深度和颜色传感器和机器人手臂的真实情况下的衣服分类任务中应用。实验结果表明了该方法的希望，因为对于相当纠缠的初始服装配置，平均达到了超过95％的隔离一件衣服的确定性。
Rigid object manipulation with robots has mainly relied on precise, expensive models and deterministic sequences. Given the great complexity of accurately modeling deformable objects, their manipulation seems to call for a rather different approach. This paper proposes a probabilistic planner, based on a Partially Observable Markov Decision Process (POMDP), targeted at reducing the inherent uncertainty of deformable object sorting. It is shown that a small set of unreliable actions and inaccurate perceptions suffices to accomplish the task, provided faithful statistics on both of them are collected beforehand. The planner has been applied to a clothes sorting task in a real case context with a depth and color sensor and a robotic arm. Experimental results show the promise of the approach since more than 95% certainty of having isolated a piece of clothing is reached in an average of four steps for quite entangled initial clothing configurations.