In human-robot interaction (HRI), we study how humans interact with robots, but also the effects of robot behavior on human perception and well-being. Especially, the influence on humans by tandem robots with one human controlled and one autonomous robot or even semi-autonomous multi-robot systems is not yet fully understood. Here, we focus on a leader-follower scenario and study how emotionally expressive motion patterns of a small, mobile follower robot affect the perception of a human operator controlling the leading robot. We examined three distinct emotional behaviors for the follower compared to a neutral condition: angry, happy and sad. We analyzed how participants maneuvered the leader robot along a set path while experiencing each follower behavior in a randomized order. We identified a significant shift in attention toward the follower with emotionally expressive behaviors compared to the neutral condition. For example, the angry behavior significantly heightened participant stress levels and was considered the least preferred behavior. The happy behavior was the most preferred and associated with increased excitement by the participants. Integrating the proposed behaviors in robots can profoundly influence the human operator's attention, emotional state, and overall experience. These insights are valuable for future HRI tandem robot designs.

People employ expressive behaviors to effectively communicate and coordinate their actions with others, such as 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 adaptable and composable, building upon each other. Our approach 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 users found to be competent and easy to understand. Supplementary material can be found at https://generative-expressive-motion.github.io/.