Online Vision- and Action-Based Object Classification Using Both
  Symbolic and Subsymbolic Knowledge Representations
  If a robot is supposed to roam an environment and interact with objects, it
is often necessary to know all possible objects in advance, so that a database
with models of all objects can be generated for visual identification. However,
this constraint cannot always be fulfilled. Due to that reason, a model based
object recognition cannot be used to guide the robot's interactions. Therefore,
this paper proposes a system that analyzes features of encountered objects and
then uses these features to compare unknown objects to already known ones. From
the resulting similarity appropriate actions can be derived. Moreover, the
system enables the robot to learn object categories by grouping similar objects
or by splitting existing categories. To represent the knowledge a hybrid form
is used, consisting of both symbolic and subsymbolic representations.
