Incremental Robot Learning of New Objects with Fixed Update Time

In order for autonomous robots to operate in unstructured environments, several perceptual capabilities are required. Most of these skills cannot be hard-coded in the system beforehand, but need to be developed and learned over time as the agent explores and acquires novel experience. As a prototypical example of this setting, in this work we consider the task of visual object recognition in robotics: Images depicting different objects are received one frame at a time, and the system needs to incrementally update the internal model of known objects as new examples are gathered. In the last few years, machine learning has achieved remarkable results in a variety of applications for robotics and computer vision [1], [2], [3]. However, most of these methods have been developed for off-line (or "batch") settings, where the entire training set is available beforehand. The problem of updating a learned model online has been addressed in the literature [4], [5], [6], [7], but most algorithms proposed in this context do not take into account challenges that are characteristic of realistic lifelong learning applications. Specifically, in online classification settings, a major challenge is to cope with the situation in which a novel class is added to the model. Indeed, 1) most learning algorithms require the number of classes to be known beforehand and not grow indefinitely, and 2) the imbalance between the few examples of the new class (potentially just one) and the many examples of previously learned classes can lead to unexpected and undesired behaviors [8].