  Human-robot communication in situated environments involves a complex
interplay between knowledge representations across a wide variety of
modalities. Crucially, linguistic information must be associated with
representations of objects, locations, people, and goals, which may be
represented in very different ways. In previous work, we developed a Consultant
Framework that facilitates modality-agnostic access to information distributed
across a set of heterogeneously represented knowledge sources. In this work, we
draw inspiration from cognitive science to augment these distributed knowledge
sources with Short Term Memory Buffers to create an STM-augmented algorithm for
referring expression generation. We then discuss the potential performance
benefits of this approach and insights from cognitive science that may inform
future refinements in the design of our approach.
