Learning relational object categories using behavioral exploration and multimodal perception
Learning relational object categories using behavioral exploration and multimodal perception
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使用行为探索和多模态感知来学习关系对象类别
DOI：
10.1109/icra.2014.6907696
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发表时间：
2014
期刊：
影响因子：
--
通讯作者：
Alexander Stoytchev
中科院分区：
文献类型：
--
作者：
Jivko Sinapov;Connor Schenck;Alexander Stoytchev
本文提出了一个框架，用于学习人类提供的类别标签，描述单个对象，成对对象关系，以及对象组。该框架进行了评估使用的实验中，机器人交互式探索36个对象，颜色，重量和内容不同。所提出的方法允许机器人不仅学习描述单个对象的类别，而且学习描述具有高识别精度的对象对和组的类别。此外，通过将类别表征建立在自己的感觉运动库中，机器人能够估计两个类别在用于识别它们的行为和感觉模态方面的相似程度。最后，这种基于相似性的度量使机器人能够通过将其与一组熟悉的类别相关联来学习新类别，从而提高其识别性能。
This paper proposes a framework for learning human-provided category labels that describe individual objects, pairwise object relationships, as well as groups of objects. The framework was evaluated using an experiment in which the robot interactively explored 36 objects that varied by color, weight, and contents. The proposed method allowed the robot not only to learn categories describing individual objects, but also to learn categories describing pairs and groups of objects with high recognition accuracy. Furthermore, by grounding the category representations in its own sensorimotor repertoire, the robot was able to estimate how similar two categories are in terms of the behaviors and sensory modalities that are used to recognize them. Finally, this grounded measure of similarity enabled the robot to boost its recognition performance when learning a new category by relating it to a set of familiar categories.