  Whenever we are addressing a specific object or refer to a certain spatial
location, we are using referential or deictic gestures usually accompanied by
some verbal description. Especially pointing gestures are necessary to dissolve
ambiguities in a scene and they are of crucial importance when verbal
communication may fail due to environmental conditions or when two persons
simply do not speak the same language. With the currently increasing advances
of humanoid robots and their future integration in domestic domains, the
development of gesture interfaces complementing human-robot interaction
scenarios is of substantial interest. The implementation of an intuitive
gesture scenario is still challenging because both the pointing intention and
the corresponding object have to be correctly recognized in real-time. The
demand increases when considering pointing gestures in a cluttered environment,
as is the case in households. Also, humans perform pointing in many different
ways and those variations have to be captured. Research in this field often
proposes a set of geometrical computations which do not scale well with the
number of gestures and objects, use specific markers or a predefined set of
pointing directions. In this paper, we propose an unsupervised learning
approach to model the distribution of pointing gestures using a
growing-when-required (GWR) network. We introduce an interaction scenario with
a humanoid robot and define so-called ambiguity classes. Our implementation for
the hand and object detection is independent of any markers or skeleton models,
thus it can be easily reproduced. Our evaluation comparing a baseline computer
vision approach with our GWR model shows that the pointing-object association
is well learned even in cases of ambiguities resulting from close object
proximity.
