  Nowadays service robots are leaving the structured and completely known
environments and entering human-centric settings. For these robots, object
perception and grasping are two challenging tasks due to the high demand for
accurate and real-time responses. Although many problems have already been
understood and solved successfully, many challenges still remain. Open-ended
learning is one of these challenges waiting for many improvements. Cognitive
science revealed that humans learn to recognize object categories and grasp
affordances ceaselessly over time. This ability allows adapting to new
environments by enhancing their knowledge from the accumulation of experiences
and the conceptualization of new object categories. Inspired by this, an
autonomous robot must have the ability to process visual information and
conduct learning and recognition tasks in an open-ended fashion. In this
context, "open-ended" implies that the set of object categories to be learned
is not known in advance, and the training instances are extracted from online
experiences of a robot, and become gradually available over time, rather than
being completely available at the beginning of the learning process.
  In my research, I mainly focus on interactive open-ended learning approaches
to recognize multiple objects and their grasp affordances concurrently. In
particular, I try to address the following research questions: (i) What is the
importance of open-ended learning for autonomous robots? (ii) How robots could
learn incrementally from their own experiences as well as from interaction with
humans? (iii) What are the limitations of Deep Learning approaches to be used
in an open-ended manner? (iv) How to evaluate open-ended learning approaches
and what are the right metrics to do so?
