Ain't Misbehavin' -- Using LLMs to Generate Expressive Robot Behavior in Conversations with the Tabletop Robot Haru

arXiv:2402.11571v1 Announce Type: cross
Abstract: Social robots aim to establish long-term bonds with humans through engaging conversation. However, traditional conversational approaches, reliant on scripted interactions, often fall short in maintaining engaging conversations. This paper addresses this limitation by integrating large language models (LLMs) into social robots to achieve more dynamic and expressive conversations. We introduce a fully-automated conversation system that leverages LLMs to generate robot responses with expressive behaviors, congruent with the robot's personality. We incorporate robot behavior with two …

abstract aim arxiv behavior bonds conversation conversational conversations cs.ai cs.cl cs.ro generate humans interactions language language models large language large language models llms long-term paper robot robots social through type