  Identifying and categorizing specific robot tasks, behaviors, and resources
is an essential precursor to reproducing and evaluating robotics experiments
across laboratories and platforms. Without some means of capturing how one
environment, platform, or behavior differs from another, we cannot begin to
establish the performance impact of these changes or predict a robot's
performance in a novel environment. As a first step towards experimental
reproducibility, existing taxonomies in the field of robotics are reviewed and
common patterns of structure and form extracted, identifying both the
properties they share with traditional taxonomies and the necessary structural
elements that draw from other classification and categorization systems. The
diversity of taxonomy subjects and subsequent difficulty in harmonization of
conceptual underpinnings is noted. Robotics taxonomies are shown to be deeply
fragmented in structure and form and to require notation that can support
complex relationships.
