This paper presents a method for learning novel objects from audio-visual input. Objects are learned using out-of-vocabulary word segmentation and object extraction. The latter half of this paper is devoted to evaluations. We propose the use of a task adopted from the RoboCup@Home league as a standard evaluation for real world applications. We have implemented proposed method on a real humanoid robot and evaluated it through a task called “Supermarket”. The results reveal that our integrated system works well in the real application. In fact, our robot outperformed the maximum score obtained in RoboCup@Home 2009 competitions.
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机译：本文提出了一种从视听输入中学习新颖对象的方法。使用词汇外单词分割和对象提取来学习对象。本文的后半部分专门用于评估。我们建议使用RoboCup @ Home联盟采用的任务作为现实应用的标准评估。我们已经在真正的人形机器人上实现了建议的方法，并通过名为“超市”的任务对其进行了评估。结果表明，我们的集成系统在实际应用中运行良好。实际上，我们的机器人的表现超过了RoboCup @ Home 2009竞赛中获得的最高分。
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