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

Large-language models (<PRE_TAG><PRE_TAG>LLMs</POST_TAG></POST_TAG>) 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 <PRE_TAG><PRE_TAG>LLMs</POST_TAG></POST_TAG> 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 <PRE_TAG><PRE_TAG>LLMs</POST_TAG></POST_TAG> and for fine-tuning <PRE_TAG><PRE_TAG>LLMs</POST_TAG></POST_TAG> for use with robots.