  Continual learning refers to the ability of a biological or artificial system
to seamlessly learn from continuous streams of information while preventing
catastrophic forgetting, i.e., a condition in which new incoming information
strongly interferes with previously learned representations. Since it is
unrealistic to provide artificial agents with all the necessary prior knowledge
to effectively operate in real-world conditions, they must exhibit a rich set
of learning capabilities enabling them to interact in complex environments with
the aim to process and make sense of continuous streams of (often uncertain)
information. While the vast majority of continual learning models are designed
to alleviate catastrophic forgetting on simplified classification tasks, here
we focus on continual learning for autonomous agents and robots required to
operate in much more challenging experimental settings. In particular, we
discuss well-established biological learning factors such as developmental and
curriculum learning, transfer learning, and intrinsic motivation and their
computational counterparts for modeling the progressive acquisition of
increasingly complex knowledge and skills in a continual fashion.
