Learning organizational principles in human environments
Learning organizational principles in human environments
复制标题
学习人类环境中的组织原则
DOI：
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发表时间：
2012
期刊：
影响因子：
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通讯作者：
M. Beetz
中科院分区：
文献类型：
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作者：
M. J. Schuster;Dominik Jain;Moritz Tenorth;M. Beetz
在人类日常环境中的机器人助手的背景下，拾取和放置任务开始在技术层面得到有效解决。因此，在其他更高层次的推理任务中，把物体放在哪里或从哪里把它们捡起来的问题越来越具有实际意义。在这项工作中，我们考虑的问题，确定环境中的组织结构，即组织原则，将允许机器人推断在哪里最好地放置一个特定的，以前看不见的对象，或在哪里合理地搜索特定类型的对象给定过去的观察对象在环境中的位置分配的问题。这个问题可以合理地表述为一个分类任务。我们声称，组织原则是由相似性的概念，并提供了一个实证分析的重要性，各种功能的数据集描述厨房的组织结构。对于上述分类任务，我们比较了标准分类方法，在所有情况下达到至少79%的平均准确率。因此，我们表明，特别是，基于本体的相似性度量非常适合作为高度歧视性的功能。我们展示了使用学习模型的组织原则在厨房环境中的一个真实的机器人系统，机器人识别一个新获得的项目，确定一个合适的位置，然后相应地存储该项目。
In the context of robotic assistants in human everyday environments, pick and place tasks are beginning to be competently solved at the technical level. The question of where to place objects or where to pick them up from, among other higher-level reasoning tasks, is therefore gaining practical relevance. In this work, we consider the problem of identifying the organizational structure within an environment, i.e. the problem of determining organizational principles that would allow a robot to infer where to best place a particular, previously unseen object or where to reasonably search for a particular type of object given past observations about the allocation of objects to locations in the environment. This problem can be reasonably formulated as a classification task. We claim that organizational principles are governed by the notion of similarity and provide an empirical analysis of the importance of various features in datasets describing the organizational structure of kitchens. For the aforementioned classification tasks, we compare standard classification methods, reaching average accuracies of at least 79% in all scenarios. We thereby show that, in particular, ontology-based similarity measures are well-suited as highly discriminative features. We demonstrate the use of learned models of organizational principles in a kitchen environment on a real robot system, where the robot identifies a newly acquired item, determines a suitable location and then stores the item accordingly.