  Nowadays, robots become a companion in everyday life. To be well-accepted by
humans, robots should efficiently understand meanings of their partners'
motions and body language, and respond accordingly. Learning concepts by
imitation brings them this ability in a user-friendly way.
  This paper presents a fast and robust model for Incremental Learning of
Concepts by Imitation (ILoCI). In ILoCI, observed multimodal spatio-temporal
demonstrations are incrementally abstracted and generalized based on both their
perceptual and functional similarities during the imitation. In this method,
perceptually similar demonstrations are abstracted by a dynamic model of mirror
neuron system. An incremental method is proposed to learn their functional
similarities through a limited number of interactions with the teacher.
Learning all concepts together by the proposed memory rehearsal enables robot
to utilize the common structural relations among concepts which not only
expedites the learning process especially at the initial stages, but also
improves the generalization ability and the robustness against discrepancies
between observed demonstrations.
  Performance of ILoCI is assessed using standard LASA handwriting benchmark
data set. The results show efficiency of ILoCI in concept acquisition,
recognition and generation in addition to its robustness against variability in
demonstrations.
