Generative Expressive Robot Behaviors using Large Language Models

People employ expressive behaviors to effectively communicate and coordinate their actions with others, e.g., nodding to acknowledge a person glancing at them or saying "excuse me'' to pass people in a busy corridor. We would like robots to also demonstrate expressive behaviors in human-robot interaction. Prior work proposes rule-based methods that struggle to scale to new communication modalities or social situations, while data-driven methods require specialized datasets for each social situation the robot is used in. We propose to leverage the rich social context available from large language models (LLMs) and their ability to generate motion based on instructions or user preferences, to generate expressive robot motion that is daptable and composable, building upon each other. Our framework utilizes few-shot chain-of-thought prompting to translate human language instructions into parametrized control code using the robot's available and learned skills. Through user studies and simulation experiments, we demonstrate that our approach produces behaviors that are equivalent to or better than professionally animated expressive behaviors.