Physically assistive robots present an opportunity to significantly increase the well-being and independence of individuals with motor impairments or other forms of disability who are unable to complete activities of daily living. Speech interfaces, especially ones that utilize Large Language Models (LLMs), can enable individuals to effectively and naturally communicate high-level commands and nuanced preferences to robots. Frameworks for integrating LLMs as interfaces to robots for high level task planning and code generation have been proposed, but fail to incorporate human-centric considerations which are essential while developing assistive interfaces. In this work, we present a framework for incorporating LLMs as speech interfaces for physically assistive robots, constructed iteratively with 3 stages of testing involving a feeding robot, culminating in an evaluation with 11 older adults at an independent living facility. We use both quantitative and qualitative data from the final study to validate our framework and additionally provide design guidelines for using LLMs as speech interfaces for assistive robots. Videos and supporting files are located on our project website: https://sites.google.com/andrew.cmu.edu/voicepilot/

翻译：物理辅助机器人为运动障碍或其他形式残疾而无法完成日常生活活动的个体提供了显著提升福祉与独立性的机遇。语音交互界面，特别是利用大型语言模型（LLMs）的界面，能够使个体有效且自然地与机器人传达高层级指令及细致偏好。已有研究提出将LLMs集成作为机器人高层任务规划与代码生成交互界面的框架，但未能纳入以人为中心的考量，而这在开发辅助性交互界面时至关重要。本研究提出一个将LLMs作为物理辅助机器人语音交互界面的框架，该框架通过涉及喂食机器人的三个阶段迭代测试构建而成，并最终在一所独立生活机构中与11位老年人进行评估验证。我们利用最终研究的定量与定性数据验证了该框架，并进一步为将LLMs用作辅助机器人语音交互界面提供了设计准则。视频及支撑文件详见项目网站：https://sites.google.com/andrew.cmu.edu/voicepilot/