  Artificial object perception usually relies on a priori defined models and
feature extraction algorithms. We study how the concept of object can be
grounded in the sensorimotor experience of a naive agent. Without any knowledge
about itself or the world it is immersed in, the agent explores its
sensorimotor space and identifies objects as consistent networks of
sensorimotor transitions, independent from their context. A fundamental drive
for prediction is assumed to explain the emergence of such networks from a
developmental standpoint. An algorithm is proposed and tested to illustrate the
approach.
