Surprise-driven acquisition of visual object representations for cognitive mobile robots

Robots in a household environment have to execute a variety of tasks including carrying objects. In order to grasp the correct objects for a desired action it is indispensable that the robot is able to recognize the objects in its environment. From time to time, the robot will encounter new unknown objects which it has never seen before. In order to recognize them in a later task the robot has to acquire an internal representation of them. In this paper, we present an approach for the autonomous acquisition of visual object representations in a cluttered environment. Guided by surprise, the robot detects novel objects in a familiar environment, selects local image features which represent their appearance and stores them in a database. Experimental results show that our method for the detection of surprising events reliably directs the robot's attention to the novel objects and that the recognition behavior based on our acquired object representations outperforms a state-of-the-art approach.