This study investigates whether emotions can be recognized through LED-only visual cues using color and animation patterns. 31 participants viewed six short videos representing basic emotions(Happiness, Sadness, Anger, Fear, Disgust and Surprise) and identified the emotion conveyed. Anger and Sadness were most accurately recognized, while Disgust and Fear were frequently misinterpreted. Participants cited color, blinking speed, and pattern rhythm as key influences. These findings highlight the potential of minimalist LED displays as lightweight, non-verbal communication tools in human-robot interaction, particularly for expressing high-arousal emotions.

Robotic musicians such as Keirzo present a provocative test‑case for the growing use of social robots in education. Music learning is highly creative and relational, raising the question of whether parents—key gatekeepers for child technology use— could accept robots as credible teachers. I conducted a focused survey study in which fifty‑three parents (Mage=44.6 years; 64% female) watched four short videos of Keirzo tutoring an eight‑year‑old drummer and then completed the General Attitudes Towards Robots Scale (GAToRS) and an adapted Parents’ Perceptions of Educational Apps Use questionnaire (PEAU‑p). Quantitative scores revealed moderately positive personal feelings toward robots but lingering doubts about pedagogical effectiveness. Thematic analysis of open responses showed cautious optimism: parents praised robots’ patience and non‑judgemental feedback, yet worried about emotional intelligence, safety, and the irreplaceable social role of human teachers. I discuss design implications for mechanomorphic music tutors and chart research directions for sustainable human–robot partnerships in creative education.

We present ViolinBrush, a real‑time interactive system that transfers the nuanced bowing gestures of a live violinist to the strokes of a robotic painting arm. Unlike previous creative‑robotics projects that treat the machine as an autonomous or pre‑programmed agent, ViolinBrush is conceived as an experiment into whether embodied musical expressiveness can imbue a robot with a perceived humanness. Continuous descriptors of bow pitch and roll are mapped, respectively, to stroke stipple density and jitter, allowing micro-fluctuations in performance energy to visibly texture each mark.

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.

As AI-driven robots become more integrated into daily life, understanding user perceptions is crucial for improving their design and interaction. This study investigates the impact of interruptibility and response unpredictability on user engagement with Keirzo, an AI-powered musical robot. Using the Robotic Social Attributes Scale (RoSAS), participants engaged with Keirzo under two conditions: one allowing interruptions and one requiring them to wait for complete responses. Findings suggest that while the ability to interrupt offered a greater sense of control, it did not significantly increase engagement. Participants generally rated Keirzo as more competent when its responses were structured and coherent, whereas repetitive or unpredictable replies reduced perceived intelligence. Perceptions of personality were mixed; some found the robot engaging and expressive, while others viewed it as mechanical or detached. These results highlight the importance of balancing control, coherence, and expressiveness in AI-driven musical interactions. As the findings are exploratory, future work should involve more adaptive systems and larger sample sizes to further examine these dynamics in creative HRI contexts.

Abstract This study explores the use of sonification in improving the In Vitro Fertilization (IVF) experience for expectant parents. The authors propose that the sonification of IVF videos, depicting the division of embryonic cells, can aid in enhancing understanding of embryonic development and foster emotional connections with medical imagery. To demonstrate this concept, the authors developed a system for creating emotional music synchronized with IVF videos, using a combination of rule-based methods and a neural network. The system was evaluated through two studies, including an online survey and a semi-structured interview, with participants being shown the sonified IVF videos. Results indicated a strong preference for videos with sonification among expectant mothers and a perceived relationship between the sonification and the video. The study supports previous findings on the trade-off between aesthetics and information conveyed through sonification, and suggests that sonification can improve the overall experience and stimulate curiosity without necessarily imparting new information about the embryos.

This paper describes a prototype audio-visual performance of a Ufactory Uarm Swift and a live musician. In this setting, the robotic arm was used as an AI agent to create a visual representation of a musical work in real-time. An A4 white canvas was gradually filled with a mixture of black, blue, red and yellow paints across the span of approximately eight minutes. The musician, performing on an acoustic violin, fitted with a custom built audio interface, performed multiple versions of an improvisatory work developed specifically for the prototype performance. The following sections discuss our technical approach to programming and implementing the Ufactory Uarm Swift as a painting arm, reflections of the musical process and propose future directions for this project.

The majority of research at the intersection of AI and video games focuses on developing agents capable of playing games without human input, or developing AI game enemies. The research in this paper explores a counter approach, whereby a player trains an a AI partner during game play and learns to play cooperatively with the agent. We created a 2D video game that allows the player to cooperate with an AI agent manipulated by two underlying algorithms, either with reinforcement learning or a random process. For our reinforcement learning approach we used a Q-learning table, that is updated based on the player. We found that players engaged strongly with the idea of training their own custom AI agent and believe this shows significant potential for future exploration.

Lyric generation is a popular sub-field of natural language generation that has seen growth in recent years. Pop lyrics are of unique interest due to the genre’s unique style and content, in addition to the high level of collaboration that goes on behind the scenes in the professional pop songwriting process. In this paper, we present a collaborative line-level lyric generation system that utilizes transfer-learning via the T5 transformer model, which, till date, has not been used to generate pop lyrics. By working and communicating directly with professional songwriters, we develop a model that is able to learn lyrical and stylistic tasks like rhyming, matching line beat requirements, and ending lines with specific target words. Our approach compares favorably to existing methods for multiple datasets and yields positive results from our online studies and interviews with industry songwriters.

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.

Current research in human-agent interaction primarily focuses on short term interaction and rarely addresses day to day use. We propose a prototype system based on a genetic algorithm that places long term interaction as the core design goal. The goal of this system is to develop stand alone long-term development and provide a platform for future post-processing of deep learning generations. This paper addresses these issues through the domain of musical interaction and improvisation, a field that incorporates dialogue-like interaction built on stylistic constraints. We contend that the objectives of continual knowledge development and building relationships are key to long-term human interaction, and design the genetic algorithm specifically around these concepts. Our eventual goal of the prototype is a future application of post processing for deep learning generative systems.

As emotion plays a growing role in robotic research it is crucial to develop methods to analyze and compare among the wide range of approaches. To this end we present a survey of 1427 IEEE and ACM publications that include robotics and emotion. This includes broad categorizations of trends in emotion input analysis, robot emotional expression, studies of emotional interaction and models for internal processing. We then focus on 232 papers that present internal processing of emotion, such as using a human's emotion for better interaction or turning environmental stimuli into an emotional drive for robotic path planning. We conducted constant comparison analysis of the 232 papers and arrived at three broad categorization metrics - emotional intelligence, emotional model and implementation - each including two or three subcategories. The subcategories address the algorithm used, emotional mapping, history, the emotional model, emotional categories, the role of emotion, the purpose of emotion and the platform. Our results show a diverse field of study, largely divided by the role of emotion in the system, either for improved interaction, or improved robotic performance. We also present multiple future opportunities for research and describe intrinsic challenges common in all publications.

We present a divergent approach to robotic sonification with the goal of improving the quality and safety of human-robot interactions. Sonification (turning data into sound) has been underutilized in robotics, and has broad potential to convey robotic movement and intentions to users without requiring visual engagement. We design and evaluate six different sonifications of movements for a robot with four degrees of freedom. Our sonification techniques include a direct mapping from each degree of freedom to pitch and timbre changes, emotion-based sound mappings, and velocity-based mappings using different types of sounds such as motors and music. We evaluate these sonifications using metrics for ease of use, enjoyment/appeal, and conveyance of movement information. Based on our results, we make recommendations to inform decisions for future robot sonification design. We suggest that when using sonification to improve safety of human-robot collaboration, it is necessary not only to convey sufficient information about movements, but also to convey that information in a pleasing and even social way to to enhance the human-robot relationship.

As human-robot collaboration opportunities continue to expand, trust becomes ever more important for full engagement and utilization of robots. Affective trust, built on emotional relationship and interpersonal bonds is particularly critical as it is more resilient to mistakes and increases the willingness to collaborate. In this paper we present a novel model built on music-driven emotional prosody and gestures that encourages the perception of a robotic identity, designed to avoid uncanny valley. Symbolic musical phrases were generated and tagged with emotional information by human musicians. These phrases controlled a synthesis engine playing back pre-rendered audio samples generated through interpolation of phonemes and electronic instruments. Gestures were also driven by the symbolic phrases, encoding the emotion from the musical phrase to low degree-of-freedom movements. Through a user study we showed that our system was able to accurately portray a range of emotions to the user. We also showed with a significant result that our non-linguistic audio generation achieved an 8% higher mean of average trust than using a state-of-the-art text-to-speech system.