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.

在本文中，我们对 LLMs 在 SAR 技术中的应用进行了简要调查，并讨论了将 LLMs 应用于 SAR 的三个主要技术挑战（自然语言对话、多模态理解和 LLMs 作为机器人策略）的潜力和风险。

大型语言模型如何促进更好的社交辅助人机交互：简要调查

How Can Large Language Models Enable Better Socially Assistive  Human-Robot Interaction: A Brief Survey

Autonomous robot systems for applications from search and rescue to assistive
guidance should be able to engage in natural language dialog with people. To
study such cooperative communication, we introduce Robot Simultaneous
Localization and Mapping with Natural Language (RobotSlang), a benchmark of 169
natural language dialogs between a human Driver controlling a robot and a human
Commander providing guidance towards navigation goals. In each trial, the pair
first cooperates to localize the robot on a global map visible to the
Commander, then the Driver follows Commander instructions to move the robot to
a sequence of target objects. We introduce a Localization from Dialog History
(LDH) and a Navigation from Dialog History (NDH) task where a learned agent is
given dialog and visual observations from the robot platform as input and must
localize in the global map or navigate towards the next target object,
respectively. RobotSlang is comprised of nearly 5k utterances and over 1k
minutes of robot camera and control streams. We present an initial model for
the NDH task, and show that an agent trained in simulation can follow the
RobotSlang dialog-based navigation instructions for controlling a physical
robot platform. Code and data are available at this https URL

该论文介绍了一种基于自然语言对话的机器人定位与导航任务基准测评 RobotSlang，并提出了从对话历史记录中学习的自适应定位与导航。研究结果表明，通过该方法可以训练出可控制实际机器人平台的智能体。