Dance Teaching by a Robot: Combining Cognitive and Physical Human-Robot Interaction for Supporting the Skill Learning Process

This letter presents a physical human-robot interaction scenario in which a robot guides and performs the role of a teacher within a defined dance training framework. A combined cognitive and physical feedback of performance is proposed for assisting the skill learning process. Direct contact cooperation has been designed through an adaptive impedance-based controller that adjusts according to the partner's performance in the task. In measuring performance, a scoring system has been designed using the concept of progressive teaching (PT). The system adjusts the difficulty based on the user's number of practices and performance history. Using the proposed method and a baseline constant controller, comparative experiments have shown that the PT presents better performance in the initial stage of skill learning. An analysis of the subjects' perception of comfort, peace of mind, and robot performance have shown a significant difference at the p < .01 level, favoring the PT algorithm.Comment: Presented at IEEE International Conference on Robotics and Automation ICRA-201

Towards a framework to make robots learn to dance

A key motive of human-robot interaction is to make robots and humans interact through different aspects of the real world. As robots become more and more realistic in appearance, so has the desire for them to exhibit complex behaviours. A growing area of interest in terms of complex behaviour is robot dancing. Dance is an entertaining activity that is enjoyed either by being the performer or the spectator. Each dance contain fundamental features that make-up a dance. It is the curiosity for some researchers to model such an activity for robots to perform in human social environments. From current research, most dancing robots are pre-programmed with dance motions and few have the ability to generate their own dance or alter their movements according to human responses while dancing. This thesis explores the question Can a robot learn to dance? . A dancing framework is proposed to address this question. The Sarsa algorithm and the Softmax algorithm from traditional reinforcement learning form part of the dancing framework to enable a virtual robot learn and adapt to appropriate dance behaviours. The robot follows a progressive approach, utilising the knowledge obtained at each stage of its development to improve the dances that it generates. The proposed framework addresses three stages of development of a robot s dance: learning ability; creative ability of dance motions, and adaptive ability to human preferences. Learning ability is the ability to make a robot gradually perform the desired dance behaviours. Creative ability is the idea of the robot generating its own dance motions, and structuring them into a dance. Adaptive ability is where the robot changes its dance in response to human feedback. A number of experiments have been conducted to explore these challenges, and verified that the quality of the robot dance can be improved through each stage of the robot s development

Haptic communication between partner dancers and swing as a finite state machine

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2005.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Vita.Includes bibliographical references (p. 129-138).To see two expert partners, one leading and the other following, swing dance together is to watch a remarkable two-agent communication and control system in action. Even blindfolded, the follower can decode the leader's moves from haptic cues. The leader composes the dance from the vocabulary of known moves so as to complement the music he is dancing to. Systematically addressing questions about partner dance communication is of scientific interest and could improve human-robotic interaction, and imitating the leader's choreographic skill is an engineering problem with applications beyond the dance domain. Swing dance choreography is a finite state machine, with moves that transition between a small number of poses. Two automated choreographers are presented. One uses an optimization and randomization scheme to compose dances by a sequence of shortest path problems, with edge lengths measuring the dissimilarity of dance moves to each bar of music. The other solves a two-player zero-sum game between the choreographer and a judge. Choosing moves at random from among moves that are good enough is rational under the game model.(cont.) Further, experiments presenting conflicting musical environments to two partners demonstrate that although musical expression clearly guides the leader's choice of moves, the follower need not hear the same music to properly decode the leader's signals. Dancers embody gentle interaction, in which each participant extends the capabilities of the other, and their cooperation is facilitated by a shared understanding of the motions to be performed. To demonstrate that followers use their understanding of the move vocabulary to interact better with their leaders, an experiment paired a haptic robot leader with human followers in a haptically cued dance to a swing music soundtrack. The subjects' performance differed significantly between instances when the subjects could determine which move was being led and instances when the subjects could not determine what the next move would be. Also, two-person teams that cooperated haptically to perform cyclical aiming tasks showed improvements in the Fitts' law or Schmidt's law speed-accuracy tradeoff consistent with a novel endpoint compromise hypothesis about haptic collaboration.by Sommer Elizabeth Gentry.Ph.D

An Enactivist Model of Improvisational Dance

An Enactivist Model of Improvisational Danc

A practice-inspired mindset for researching the psychophysiological and medical health effects of recreational dance (dance pport)

“Dance” has been associated with many psychophysiological and medical health effects. However, varying definitions of what constitute “dance” have led to a rather heterogenous body of evidence about such potential effects, leaving the picture piecemeal at best. It remains unclear what exact parameters may be driving positive effects. We believe that this heterogeneity of evidence is partly due to a lack of a clear definition of dance for such empirical purposes. A differentiation is needed between (a) the effects on the individual when the activity of “dancing” is enjoyed as a dancer within different dance domains (e.g., professional/”high-art” type of dance, erotic dance, religious dance, club dancing, Dance Movement Therapy (DMT), and what is commonly known as hobby, recreational or social dance), and (b) the effects on the individual within these different domains, as a dancer of the different dance styles (solo dance, partnering dance, group dance; and all the different styles within these). Another separate category of dance engagement is, not as a dancer, but as a spectator of all of the above. “Watching dance” as part of an audience has its own set of psychophysiological and neurocognitive effects on the individual, and depends on the context where dance is witnessed. With the help of dance professionals, we first outline some different dance domains and dance styles, and outline aspects that differentiate them, and that may, therefore, cause differential empirical findings when compared regardless (e.g., amount of interpersonal contact, physical exertion, context, cognitive demand, type of movements, complexity of technique and ratio of choreography/improvisation). Then, we outline commonalities between all dance styles. We identify six basic components that are part of any dance practice, as part of a continuum, and review and discuss available research for each of them concerning the possible health and wellbeing effects of each of these components, and how they may relate to the psychophysiological and health effects that are reported for “dancing”: (1) rhythm and music, (2) sociality, (3) technique and fitness, (4) connection and connectedness (self-intimation), (5) flow and mindfulness, (6) aesthetic emotions and imagination. Future research efforts might take into account the important differences between types of dance activities, as well as the six components, for a more targeted assessment of how “dancing” affects the human body

Post-Cinematic Bodies

How is human embodiment transformed in an age of algorithms? How do post-cinematic media technologies such as AI, VR, and robotics target and re-shape our bodies? 'Post-Cinematic Bodies' grapples with these questions by attending both to mundane devices - such as smartphones, networked exercise machines, and smart watches and other wearables equipped with heartrate sensors - as well as to new media artworks that rework such equipment to reveal to us the ways that our fleshly existences are increasingly up for grabs. Through an equally philosophical and interpretive analysis, the book aims to develop a new aesthetics of embodied experience that is attuned to a new age of predictive technology and metabolic capitalism

An integrated theory of language production and comprehension

Currently, production and comprehension are regarded as quite distinct in accounts of language processing. In rejecting this dichotomy, we instead assert that producing and understanding are interwoven, and that this interweaving is what enables people to predict themselves and each other. We start by noting that production and comprehension are forms of action and action perception. We then consider the evidence for interweaving in action, action perception, and joint action, and explain such evidence in terms of prediction. Specifically, we assume that actors construct forward models of their actions before they execute those actions, and that perceivers of others' actions covertly imitate those actions, then construct forward models of those actions. We use these accounts of action, action perception, and joint action to develop accounts of production, comprehension, and interactive language. Importantly, they incorporate well-defined levels of linguistic representation (such as semantics, syntax, and phonology). We show (a) how speakers and comprehenders use covert imitation and forward modeling to make predictions at these levels of representation, (b) how they interweave production and comprehension processes, and (c) how they use these predictions to monitor the upcoming utterances. We show how these accounts explain a range of behavioral and neuroscientific data on language processing and discuss some of the implications of our proposal