VoicePilot: Harnessing LLMs as Speech Interfaces for Physically Assistive Robots
This work addresses the need for more natural and effective human-robot interaction in assistive settings, though it appears incremental by building on existing LLM frameworks with human-centric considerations.
The authors tackled the problem of enabling individuals with motor impairments to communicate high-level commands to assistive robots by developing a speech interface framework using Large Language Models, which they validated through a study with 11 older adults at an independent living facility.
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/