Understanding Large-Language Model (LLM)-powered Human-Robot Interaction

• Main Authors: Kim, Callie Y, Lee, Christine P, Mutlu, Bilge
• Format: Journal Article
• Language: English
• Published: 06-01-2024
• Subjects: Computer Science - Human-Computer Interaction; Computer Science - Robotics
• DOI: 10.48550/arxiv.2401.03217

Large-language models (LLMs) hold significant promise in improving
human-robot interaction, offering advanced conversational skills and
versatility in managing diverse, open-ended user requests in various tasks and
domains. Despite the potential to transform human-robot interaction, very
little is known about the distinctive design requirements for utilizing LLMs in
robots, which may differ from text and voice interaction and vary by task and
context. To better understand these requirements, we conducted a user study (n
= 32) comparing an LLM-powered social robot against text- and voice-based
agents, analyzing task-based requirements in conversational tasks, including
choose, generate, execute, and negotiate. Our findings show that LLM-powered
robots elevate expectations for sophisticated non-verbal cues and excel in
connection-building and deliberation, but fall short in logical communication
and may induce anxiety. We provide design implications both for robots
integrating LLMs and for fine-tuning LLMs for use with robots.