How Can Large Language Models Enable Better Socially Assistive Human-Robot Interaction: A Brief Survey
It addresses the problem of improving social intelligence in assistive robots for vulnerable user groups, but is incremental as it surveys existing ideas rather than proposing new solutions.
This survey examines how large language models (LLMs) can address technical challenges in socially assistive robots (SARs) for populations like older adults and children with autism, highlighting their potential to enhance natural language dialog, multimodal understanding, and robot policies while noting associated risks.
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.