Learning about objects with human teachers
Learning about objects with human teachers
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DOI：
10.1145/1514095.1514101
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发表时间：
2009-03
期刊：
影响因子：
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通讯作者：
A. Thomaz;M. Cakmak
中科院分区：
文献类型：
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作者：
A. Thomaz;M. Cakmak
机器人在新环境中的一般学习任务是学习物体以及它们提供的动作/效果。为了解决这个问题，我们研究了人类伴侣可以直观地帮助机器人学习的方法，即社会引导机器学习。我们展示了用我们的机器人Junior进行的实验，并进行了六个观察，以描述人们如何对待关于物体的教学。我们展示了Junior成功地使用透明性来减少错误。最后，我们介绍了“社会”与“非社会”数据集在训练SVM分类器时的影响。
A general learning task for a robot in a new environment is to learn about objects and what actions/effects they afford. To approach this, we look at ways that a human partner can intuitively help the robot learn, Socially Guided Machine Learning. We present experiments conducted with our robot, Junior, and make six observations characterizing how people approached teaching about objects. We show that Junior successfully used transparency to mitigate errors. Finally, we present the impact of “social” versus “non-social” data sets when training SVM classifiers.