  The emergence of large vision models (LVMs) is following in the footsteps of
the recent prosperity of Large Language Models (LLMs) in following years.
However, there's a noticeable gap in structured research applying LVMs to
human-robot interaction (HRI), despite extensive evidence supporting the
efficacy of vision models in enhancing interactions between humans and robots.
Recognizing the vast and anticipated potential, we introduce an initial design
space that incorporates domain-specific LVMs, chosen for their superior
performance over normal models. We delve into three primary dimensions: HRI
contexts, vision-based tasks, and specific domains. The empirical evaluation
was implemented among 15 experts across six evaluated metrics, showcasing the
primary efficacy in relevant decision-making scenarios. We explore the process
of ideation and potential application scenarios, envisioning this design space
as a foundational guideline for future HRI system design, emphasizing accurate
domain alignment and model selection.
