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We present our method for learning object categories from the internet using cues obtained through human-robot interaction. Such cues include an object model acquired by observation and the name of the object. Our learning approach emulates the natural learning process of children when they observe their environment, encounter unknown objects and ask adults the name of the object. Using this learning approach, our robot is able to discover objects in a domestic environment by observing when humans naturally move objects as part of their daily activities. Using speech interface, the robot directly asks humans the name of the object by showing an example of the acquired model. The name in text format and the previously learnt model serve as input parameters to retrieve object category images from a search engine, select similar object images, and build a classifier.
Christian I. Penaloza, Yasushi Mae, Kenichi Ohara, and Tatsuo Arai: "Web-Enhanced Object Category Learning from an Object Model", ACM/IEEE International Conference on Human-Robot Interaction (HRI) , pp.223, Laussanne Switzerland. March 6-9, 2011.