Ahstract- This study explores the potential of music driven sonification as an effective method for improving safety in humanrobot collaboration. Building on the rich expressive content of music, this study assesses the communicative potential of both low level musical features, such as pitch, tempo, and timbre; and high level music features of rhythmic stability and tension-release. Two music datasets have been created, labeled, and evaluated based on five criteria: safety/danger, approachability, risk of failure, and urgency. The first dataset consists of prerecorded song clips while the second one contains original compositions designed to isolate high-level musical features. 400 participants annotated our datasets base on the five criteria. Our findings reveal significant correlations between musical features such as timbre, harmonic tension, and note onset; and the perception of safety, urgency, and risk. Based on these results, we developed a framework and an audio plugin for music-driven sonification of robotic gestures to support safe human-robot interaction.

This paper investigates how melodic prediction can enhance synchronization and fluency in human-robot collaborative tasks. We leverage humans’ natural ability to anticipate musical progressions to improve the fluency of handoff interactions. Through an experiment with 21 participants performing a dual-task scenario of sorting tiles while handing objects to a robot, we compared three sonification approaches: robot consequential sounds, musical scales, and musical melodies. Participants synchronized their handoffs based on the finale of the scale/melody, and aligned their actions with the leading tone rather than the final tonic note. Results demonstrate that both musical conditions significantly improved timing accuracy and enabled participants to better perform concurrent tasks compared to motor sounds alone. Melodies proved to be statistically significantly more consistent over repeated use of the stimuli, which shows that the diverse nature of tonal melodies can improve long term interactions without sacrificing performance. These findings suggest that the predictive qualities of tonal melodies provides an effective tool for anticipatory action.

The purpose of this research is to use human motion and musical features to generate dances for non-anthropomorphic robots. Many non-humanoid dancing robots are hard-coded by their choreographer/programmer. While some robots do generate dances based on music, most of the robots are anthropomorphic and don't use human motion to enhance their dance. This research looks to introduce novel ways of generating dances for a 7 degree of freedom (DoF) robotic arm based on a variety of musical features. It also develops new methods to capture human dance motion for non-humanoid robots. Lastly, it combines human-motion influenced robotic dance with the music based generative dance using to enhance the robot's artistic expression.

Research in human-robot interaction has focused on the relationship between a single robot and a single human participant. Only limited research has addressed the contrasting dynamic when humans interact with a group of robots. This dynamic adds additional human-robot interaction considerations, such as the level of entitativity, which is the identification of a group as a single entity as opposed to a collection of individuals. This paper proposes that emotional music prosody can play a key role in improving the interaction between humans and groups of robots by modifying the level of entitativity. Musical prosody refers to the use of pitch, rhythm and timbre features derived from language, but used without semantic meaning.We conducted a between-group experiment, presenting to subjects a group of industrial robotic arms performing a task either without sound, with the same emotional musical prosody voice for each robot, or with contrasting voices for different robots. We were able to show with significant results that the use of musical prosody improved likeability and trust over soundless gestures for groups of robots. We also demonstrate that, through subtle variations, prosody is able to alter the level of entitativity perceived by external observers. Finally, our results indicate a complex relationship between entitativity and common HRI metrics with higher levels of entitativity leading to improved performance, contradicting past literature.