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Research at the intersection of robots and dance promises to create vehicles for expression that enable new creative pursuits and allow robots to function better, especially in human-facing scenarios. Moving this research beyond fringe spectacle and establishing it as a serious, systematic field—a proper subdiscipline of both robotics and dance—will require answering a key question: How does dance advance the fundamentals of robotics, and vice versa? Focusing on the former, this article offers glimpses of this new field with examples of meaningful contributions to control, robotics, and autonomous systems, such as novel actuator designs, improved sensing systems, salient motion profiles for robots, reproducible experiment designs, and new theories of motion derived from the study of dance. It also poses two grand challenges for the emerging field of choreobotics: developing a robust symbolic system for representing bodily action and establishing rich, repeatable testing environments for human–robot interaction. 

How do we make a machine that indicates changes to its internal state, e.g., status, goals, attitude, or even emotion, through changes in movement profiles? This workshop will pose a possible direction toward such ends that leverages movement notation as a source for clearly defining abstract concepts of similarity and symbolic representation of the parts and patterns of movement - in order to identify, record and interpret patterns of human movement on both the micro and macro levels. First, we will move together. This will activate an innate ability to imitate each other and, in doing so, illuminate the principal components of Laban/Bartenieff Movement Studies – a field comprised of Laban Movement Analysis and Bartenieff Fundamentals – and the Body, Effort, Shape, Space, and Time (BESST) System of movement analysis. This system of work, deriving from dance and physical therapy practices, which is a textbook; thus, a key value proposition of the workshop is in its embodied, situated nature that can be supplemented by textbooks, including a newly released book from MIT Press authored by the workshop organizers. Next, we will try to write down what we’re doing. A set of symbols for describing elements of the BESST System, which seem to be particularly perceptually meaningful to human observers, will be presented so that movement ideas can be notated and, thus, translated between bodies. We will explore both Labanotation and a related “motif”-style notation. This workshop is supported by NSF grant numbers 2234195 and 2234197. 

Affective movement will likely be an important component of robotic interaction as more and more robots move into human-facing scenarios where humans are (consciously or unconsciously) constantly monitoring the motion profile of counterparts in order to make judgments about the state of their counterpart. Many current studies in affective movement recognition and generation seek to either increase a machine’s ability to correctly identify human affect or to identify and create components of robotic movement that enhance human perception. However, very few of these studies investigate the influence of environmental context on a machine’s ability to correctly identity human affect or a human’s ability to correctly identify the affective intent of a robot. This paper presents the results of a user study that investigated how human perception of stylized walking sequences (created in [1]) varied based on the environment where they were portrayed. The results show that environment context can impact a person’s ability to correctly perceive the intended style of a movement.