Expressive robot motion can help establish acceptance of this technology in everyday life, but understanding what makes movement expressive is a complex and multifaceted task. This paper presents the results of an online study with 46 participants, it aims to explore how people perceive and interpret the expressive qualities of human movement and how they envision the translation of their description into an imagined non-humanoid, quadrupedal robot. Through a qualitative analysis of responses, we conceptualize three themes: their understanding of intent, their interpretations of movement qualities, and finally, their translation from human to robot movement. Respondents’ descriptions of their initial understanding of the performer’s intent fall into two modes, bio-mechanical and narrative. We illustrate their interpretations of movement qualities through four strategies: movement features as kinematic indicators, intent indicators, attributed context, and perceived internal states. Lastly, we observe their translation from human to robot movement, with a particular focus on respondents’ use of kinaesthetic empathy and anthropomorphism. Our findings aim to support a bottom-up approach, using users’ general knowledge for designing expressive robot motion.

What expectations exist in the minds of dancers when interacting with a generative machine learning model? During two workshop events, experienced dancers explore these expectations through improvisation and role-play, embodying an imagined AI-dancer. The dancers explored how intuited flow, shared images, and the concept of a human replica might work in their imagined AI-human interaction. Our findings challenge existing assumptions about what is desired from generative models of dance, such as expectations of realism, and how such systems should be evaluated. We further advocate that such models should celebrate non-human artefacts, focus on the potential for serendipitous moments of discovery, and that dance practitioners should be included in their development. Our concrete suggestions show how our findings can be adapted into the development of improved generative and interactive machine learning models for dancers’ creative practice.

Through dance, a wide range of emotions can be expressed. As virtual agents and robots continue to become part of our daily lives, the need for them to efficiently convey emotion and intent increases. When trained to dance, to what extent can AI learn to model the tacit mappings between sound and motion? Here, we explore the creative capacity of a generative model trained on 3D motion capture recordings of improvised dance. We perform a perceptual judgment experiment wherein respondents rate movement generated by our model as well as human performances. While the sound-motion mappings remain somewhat elusive, particularly when compared to examples of human dance, our study shows that in certain aspects related to perceived dance-likeness and expressivity, the model successfully mimics human dance movement. By employing a perceptual study to evaluate our generative model, we aim to further our ability to understand the affordances and limitations of creative AI.