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：摘要：物理辅助机器人为显著提升运动障碍或其他残疾人群的福祉与独立性提供了机遇，使其能够完成日常生活活动。语音接口，尤其是利用大语言模型（LLMs）的接口，能让用户高效且自然地向机器人传达高级指令与细微偏好。目前已有将LLMs集成作为机器人接口以实现高级任务规划与代码生成的框架，但这些框架未能纳入开发辅助接口时至关重要的人本考量。本研究提出一个将LLMs作为物理辅助机器人语音接口的框架，通过涉及喂食机器人的三阶段迭代测试构建，最终在独立生活设施中与11名老年人共同完成评估。我们利用最终研究的定量与定性数据验证了该框架，并进一步提出了将LLMs用作辅助机器人语音接口的设计指南。视频及辅助文件见项目网站：https://sites.google.com/andrew.cmu.edu/voicepilot/