  Successful human-robot cooperation hinges on each agent's ability to process
and exchange information about the shared environment and the task at hand.
Human communication is primarily based on symbolic abstractions of object
properties, rather than precise quantitative measures. A comprehensive robotic
framework thus requires an integrated communication module which is able to
establish a link and convert between perceptual and abstract information.
  The ability to interpret composite symbolic descriptions enables an
autonomous agent to a) operate in unstructured and cluttered environments, in
tasks which involve unmodeled or never seen before objects; and b) exploit the
aggregation of multiple symbolic properties as an instance of ensemble
learning, to improve identification performance even when the individual
predicates encode generic information or are imprecisely grounded.
  We propose a discriminative probabilistic model which interprets symbolic
descriptions to identify the referent object contextually w.r.t.\ the structure
of the environment and other objects. The model is trained using a collected
dataset of identifications, and its performance is evaluated by quantitative
measures and a live demo developed on the PR2 robot platform, which integrates
elements of perception, object extraction, object identification and grasping.
