While today's robots are able to perform sophisticated tasks, they can only
act on objects they have been trained to recognize. This is a severe
limitation: any robot will inevitably see new objects in unconstrained
settings, and thus will always have visual knowledge gaps. However, standard
visual modules are usually built on a limited set of classes and are based on
the strong prior that an object must belong to one of those classes.
Identifying whether an instance does not belong to the set of known categories
(i.e. open set recognition), only partially tackles this problem, as a truly
autonomous agent should be able not only to detect what it does not know, but
also to extend dynamically its knowledge about the world. We contribute to this
challenge with a deep learning architecture that can dynamically update its
known classes in an end-to-end fashion. The proposed deep network, based on a
deep extension of a non-parametric model, detects whether a perceived object
belongs to the set of categories known by the system and learns it without the
need to retrain the whole system from scratch. Annotated images about the new
category can be provided by an 'oracle' (i.e. human supervision), or by
autonomous mining of the Web. Experiments on two different databases and on a
robot platform demonstrate the promise of our approach.

提出一种基于深度学习架构的机器人知识动态更新方法，该方法可以在不重新训练整个系统的情况下检测和学习感知对象是否属于系统已知的类别集，并可通过手动标注或网络自动挖掘获得新的类别信息以扩展系统知识。