Task-Specific Novel Object Characterization

Abstract

In an open world, a robot encounters novel objects. It needs to be able to deal with such novelties. For instance, to characterize the object, if it is similar to a known object, or to indicate that an object is unknown or irrelevant. We present an approach for robots to deal with an open world in which objects are encountered that are not known beforehand. Our method first decides whether an object is relevant for the task at hand, if it is similar to an object that is known to be relevant. Relevancy is determined from a task-specific taxonomy of objects. If the object is relevant for the task, then it is characterized through the taxonomy. The task determines the level of detail that is needed, which relates to the levels in the taxonomy. The advantage of our method is that it only needs to model the relevant objects and not all possible irrelevant and often unknown objects that the robot may also encounter. We show the merit of our method in a real-life experiment of a search and rescue task in a messy and cluttered house, where victims (including novelties) were successfully found.