Augmenting Robot Knowledge Consultants with Distributed Short Term Memory
This work addresses communication challenges in situated human-robot interaction, but it appears incremental as it builds on prior work without demonstrating major breakthroughs.
The authors tackled the problem of human-robot communication by augmenting a distributed knowledge framework with short-term memory buffers to improve referring expression generation, though no concrete performance numbers are provided.
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.