| Summary: | With the developments in the field of Artificial Intelligence (AI), robots are becoming smarter, more efficient and capable of doing more dififcult tasks than before. Recent progress in Machine Learning has revolutionized the field of AI. Rather than performing pre-programmed tasks, nowadays robots are learning things, and becoming more autonomous along the way. However, in most of the cases, robots need a certain level of human assistance to learn something. To recognize or classify daily objects is a very important skill that a service robot should possess. In this research work, we have implemented a fully autonomous object category learning system for service robots, where the robot uses internet resources to learn object categories. It gets the name of an unknown object by performing reverse image search in the internet search engines, and applying a verification strategy afterwards. Then the robot retrieves a number of images of that object from internet and use those to generate training data for learning classifiers. The implemented system is tested in actual domestic environment. The classification performance is examined against some object categories from a benchmark dataset. The system performed decently with 78:40% average accuracy on ve object categories taken from the benchmark dataset and showed promising results in real domestic scenarios. There are existing research works that deal with object category learning for robots using internet images. But those works use Human-in-the-loop models, where humans assist the robot to get the object name for using it as a search cue to retrieve training images from internet. Our implemented system eliminates the necessity of human assistance by making the task of object name determination automatic. This facilitates the whole process of learning object categories with full autonomy, which is the main contribution of this research.
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