  Socially assistive robots (SARs) have shown great success in providing
personalized cognitive-affective support for user populations with special
needs such as older adults, children with autism spectrum disorder (ASD), and
individuals with mental health challenges. The large body of work on SAR
demonstrates its potential to provide at-home support that complements
clinic-based interventions delivered by mental health professionals, making
these interventions more effective and accessible. However, there are still
several major technical challenges that hinder SAR-mediated interactions and
interventions from reaching human-level social intelligence and efficacy. With
the recent advances in large language models (LLMs), there is an increased
potential for novel applications within the field of SAR that can significantly
expand the current capabilities of SARs. However, incorporating LLMs introduces
new risks and ethical concerns that have not yet been encountered, and must be
carefully be addressed to safely deploy these more advanced systems. In this
work, we aim to conduct a brief survey on the use of LLMs in SAR technologies,
and discuss the potentials and risks of applying LLMs to the following three
major technical challenges of SAR: 1) natural language dialog; 2) multimodal
understanding; 3) LLMs as robot policies.
