Online unsupervised cumulative learning for life-long robot operation

The effective life-long operation of service robots and assistive companions depends on the robust ability of the system to learn cumulatively and in an unsupervised manner. For a cumulative learning robot there are particular characteristics that the system should have, such as being able to detect new perceptions, being able to learn online and without supervision, expand when required, etc. Bag-of-Words is a generic and compact representation of visual perceptions which has commonly and successfully been used in object recognition problems. However in its original form, it is unable to operate online and expand its vocabulary when required. This paper describes a novel method for cumulative unsupervised learning of objects by visual inspection, using an online and expanding when required Bag-of-Words. We present a set of experiments with a real-world robot, which cumulatively learns a series of objects. The results show that the system is able to learn cumulatively and recall correctly the objects it was trained on.