  Within the human-robot interaction (HRI) community, many researchers have
focused on the careful design of human-subjects studies. However, other parts
of the community, e.g., the technical advances community, also need to do
human-subjects studies to collect data to train their models, in ways that
require user studies but without a strict experimental design. The design of
such data collection is an underexplored area worthy of more attention. In this
work, we contribute a clearly defined process to collect data with three steps
for machine learning modeling purposes, grounded in recent literature, and
detail an use of this process to facilitate the collection of a corpus of
referring expressions. Specifically, we discuss our data collection goal and
how we worked to encourage well-covered and abundant participant responses,
through our design of the task environment, the task itself, and the study
procedure. We hope this work would lead to more data collection formalism
efforts in the HRI community and a fruitful discussion during the workshop.
