  The definition of symbolic descriptions that consistently represent relevant
geometrical aspects in manipulation tasks is a challenging problem that has
received little attention in the robotic community. This definition is usually
done from an observer perspective of a finite set of object relations and
orientations that only satisfy geometrical constraints to execute experiments
in laboratory conditions. This restricts the possible changes with manipulation
actions in the object configuration space to those compatible with that
particular external reference definitions, which greatly limits the spectrum of
possible manipulations. To tackle these limitations we propose an
object-centered representation that permits characterizing a much wider set of
possible changes in configuration spaces than the traditional observer
perspective counterpart. Based on this representation, we define universal
planning operators for picking and placing actions that permits generating
plans with geometric and force consistency in manipulation tasks. This
object-centered description is directly obtained from the poses and bounding
boxes of objects using a novel learning mechanisms that permits generating
signal-symbols relations without the need of handcrafting these relations for
each particular scenario.
