  Artificial self-perception is the machine ability to perceive its own body,
i.e., the mastery of modal and intermodal contingencies of performing an action
with a specific sensors/actuators body configuration. In other words, the
spatio-temporal patterns that relate its sensors (e.g. visual, proprioceptive,
tactile, etc.), its actions and its body latent variables are responsible of
the distinction between its own body and the rest of the world. This paper
describes some of the latest approaches for modelling artificial body
self-perception: from Bayesian estimation to deep learning. Results show the
potential of these free-model unsupervised or semi-supervised
crossmodal/intermodal learning approaches. However, there are still challenges
that should be overcome before we achieve artificial multisensory body
perception.
