  The thesis contributes in several important ways to the research area of 3D
object category learning and recognition. To cope with the mentioned
limitations, we look at human cognition, in particular at the fact that human
beings learn to recognize object categories ceaselessly over time. This ability
to refine knowledge from the set of accumulated experiences facilitates the
adaptation to new environments. Inspired by this capability, we seek to create
a cognitive object perception and perceptual learning architecture that can
learn 3D object categories in an open-ended fashion. In this context,
``open-ended'' implies that the set of categories to be learned is not known in
advance, and the training instances are extracted from actual experiences of a
robot, and thus become gradually available, rather than being available since
the beginning of the learning process. In particular, this architecture
provides perception capabilities that will allow robots to incrementally learn
object categories from the set of accumulated experiences and reason about how
to perform complex tasks. This framework integrates detection, tracking,
teaching, learning, and recognition of objects. An extensive set of systematic
experiments, in multiple experimental settings, was carried out to thoroughly
evaluate the described learning approaches. Experimental results show that the
proposed system is able to interact with human users, learn new object
categories over time, as well as perform complex tasks. The contributions
presented in this thesis have been fully implemented and evaluated on different
standard object and scene datasets and empirically evaluated on different
robotic platforms.
