Web mining driven object locality knowledge acquisition for efficient robot behavior

As an important information resource, visual perception has been widely employed for various indoor mobile robots. The common-sense knowledge about object locality (CSOL), e.g. a cup is usually located on the table top rather than on the floor and vice versa for a trash bin, is a very helpful context information for a robotic visual search task. In this paper, we propose an online knowledge acquisition mechanism for discovering CSOL, thereby facilitating a more efficient and robust robotic visual search. The proposed mechanism is able to create conceptual knowledge with the information acquired from the largest and the most diverse medium - the Internet. Experiments using an indoor mobile robot demonstrate the efficiency of our approach as well as reliability of goal-directed robot behaviour.