Explore the vast range of titles available.

Dance is an art and when technology meets this kind of art, it is a novel attempt in itself. Many researchers have attempted to automate several aspects of dance, right from dance notation to choreography; from dance capturing to dance generation. We define and illustrate the concept of ‘Dance Automation’ in this paper. Furthermore, we have encountered several applications of dance automation like e-learning, heritage preservation, medical therapy, etc. Despite decades of continuous attempts by many researchers in various styles of dance all round the world, we found a review paper that portrays the research status in this area of ‘dance and computers’ dating to 1990 (Leonardo 1990 Computers and dance: A bibliography, pp. 87–90). Hence, we decided to compose a comprehensive review article that showcases several aspects of dance automation and document contributions of researchers in marrying creativity with logic. This paper is an attempt to review research work reported in the literature, categorize and group significant research work completed in a span of 1967–2020 in the field of automating dance. We have explicitly identified six major categories corresponding to the use of computers in dance automation, namely, dance representation, dance capturing, dance semantics, dance generation, dance processing approaches and applications of dance automation systems. We classified several research papers under these categories according to their research approach and functionality. With the help of proposed categories and subcategories, one can easily determine the state of research and the new avenues left for exploration in the field of dance automation.

Aesthetic ability is an advanced cognitive function of human beings. Human dancers in front of mirrors estimate the aesthetics of their own dance poses by fusing multimodal information (visual and non-visual) to improve their dancing performances. Similarly, if a robot could perceive the aesthetics of its own dance poses, the robot could demonstrate more autonomous and humanoid behavior during robotic dance creation. Therefore, we propose a novel automatic approach to estimate the aesthetics of robotic dance poses by fusing multimodal information. From the visual channel, the shape features (including eccentricity, density, rectangularity, aspect ratio, Hu-moment Invariants, and complex coordinate based Fourier descriptors) are extracted from an image; from the non-visual channel, joint motion features are obtained from the internal kinestate of a robot. The above two categories of features are fused to portray completely a robotic dance pose. To automatically estimate the aesthetics of robotic dance poses, the following ten machine learning methods are deployed: Naive Bayes, Bayesian logistic regression, SVM, RBF network, ADTree, random forest, voted perceptron, KStar, DTNB, and bagging. Experimental results show the feasibility and good performance of the proposed mechanism, which was implemented in a simulated robot environment. The highest correct ratio of aesthetic evaluation is 81.6%, which comes from the ADTree, based on the above mixed features (joint + shape).

Imitation of human behaviors is one of the effective ways to develop artificial intelligence. Human dancers, standing in front of a mirror, always achieve autonomous aesthetics evaluation on their own dance motions, which are observed from the mirror. Meanwhile, in the visual aesthetics cognition of human brains, space and shape are two important visual elements perceived from motions. Inspired by the above facts, this paper proposes a novel mechanism of automatic aesthetics evaluation of robotic dance motions based on multiple visual feature integration. In the mechanism, a video of robotic dance motion is firstly converted into several kinds of motion history images, and then a spatial feature (ripple space coding) and shape features (Zernike moment and curvature-based Fourier descriptors) are extracted from the optimized motion history images. Based on feature integration, a homogeneous ensemble classifier, which uses three different random forests, is deployed to build a machine aesthetics model, aiming to make the machine possess human aesthetic ability. The feasibility of the proposed mechanism has been verified by simulation experiments, and the experimental results show that our ensemble classifier can achieve a high correct ratio of aesthetics evaluation of 75%. The performance of our mechanism is superior to those of the existing approaches.

Inspired by human dancers who can evaluate the aesthetics of their own dance poses through mirror observation, this paper presents a corresponding mechanism for robots to improve their cognitive and autonomous abilities. Essentially, the proposed mechanism is a brain-like intelligent system that is symmetrical to the visual cognitive nervous system of the human brain. Specifically, a computable cognitive model of visual aesthetics is developed using the two important aesthetic cognitive neural models of the human brain, which is then applied in the automatic aesthetics evaluation of robotic dance poses. Three kinds of features (color, shape and orientation) are extracted in a manner similar to the visual feature elements extracted by human brains. After applying machine learning methods in different feature combinations, machine aesthetics models are built for automatic evaluation of robotic dance poses. The simulation results show that our approach can process visual information effectively by cognitive computation, and achieved a very good evaluation performance of automatic aesthetics.

As gestures play an important role in human communication, there have been a number of service robots equipped with a pair of human-like arms for gesture-based human–robot interactions. However, the arms of most human companion robots are limited to slow and simple gestures due to the low maximum velocity of the arm actuators. In this work, we present the JF-2 robot, a mobile home service robot equipped with a pair of torque-controlled anthropomorphic arms. Thanks to the low inertia design of the arm, responsive Quasi-Direct Drive (QDD) actuators, and active compliant control of the joints, the robot can replicate fast human dance motions while being safe in the environment. In addition to the JF-2 robot, we also present the JF-mini robot, a scaled-down, low-cost version of the JF-2 robot mainly targeted for commercial use at kindergarten and childcare facilities. The suggested system is validated by performing three experiments, a safety test, teaching children how to dance along to the music, and bringing a requested item to a human subject.

Informed by scholarship in dance studies, this essay examines the popular phenomenon of the dancing robot. It begins with an analysis of social robotics experiments that use techniques of contemporary experimental theater to frame human–robot interactions. With elements of theater history in mind, it becomes evident that such experimental designs fruitfully destabilize common understandings of social robots, theatrical performance, and dance movement. This sets up a discussion of a co-creative approach to developing robot choreography which utilizes compositional techniques from experimental dance to, among other things, avoid cultural appropriation. Taken together, these points show that, because dance movement is culturally laden, scholarship in dance studies should be considered when designing dancing robots.

Honeybees perform what's called a \"waggle dance\" to signal where hive-mates can find nectar-rich flowers. The direction their backsides waggle and how long the dance lasts convey the location and time it will take to reach the buds. Engineering researchers at the Indian Institute of Science and the University of Maryland have borrowed those moves to teach teams of robots to communicate with each other in disaster zones or other areas where digital networks are unavailable. As explained in a paper published in Frontiers in Robotics and AI, the researchers came up with a test using a simple task: where to move a package inside a warehouse. First, a human uses hand gestures to tell a \"messenger robot\" where the package belongs. The messenger interprets the gestures using its onboard camera and skeletal tracking-system algorithms. To relay the information to a \"handling robot,\" the messenger traces a shape on the ground whose orientation indicates which direction the handler must take to reach the location. The time the messenger takes to complete the trace indicates the destination's distance. So far, the method has worked between 90 and 93.3 percent of the time in simulations and tests with humans and robots.

Clark and Fischer's depiction hypothesis is based on examples of western mimetic art. Yet social robots do not depict social interactions, but instead perform them. Similarly, dance and performance art do not rely on depiction. Kinematics and expressivity are better predictors of dance aesthetics and of effective social interactions. In this way, social robots are more like dancers than actors.

Creative movement, in the form of music- and dance-based exercise and rehabilitation, can serve as a model for learning and memory, visuospatial orientation, mental imagery, and multimodal sensory-motor integration. This review summarizes the advancement in cognitive neuroscience aimed at determining cognitive processes and brain structural and functional correlates involved in dance or creative movement, as well as the cognitive processes which accompany such activities. We synthesize the evidence for the use of cognitive, motor, and cognitive-motor function in dance as well as dance’s potential application in neurological therapy and neurorehabilitation. Finally, we discuss how partnered interaction and sensorimotor integration in dance, and “dancing robots” could shed light on future application of dance as rehabilitation, of dance used in technology and potential mechanisms of benefit from dance-based activities.

The human mind can be thought of as a black box, where the external inputs are elaborated in an unknown way and lead to external outputs. D’Ariano and Faggin schematized thinking and consciousness through quantum state dynamics. The complexity of mental states can be formalized through the entanglement of the so-called qualia states. Thus, the interaction between the mind and the external world can be formalized as an interplay between classical and quantum-state dynamics. Since quantum computing is more and more often being applied to robots, and robots constitute a benchmark to test schematic models of behavior, we propose a case study with a robotic dance, where the thinking and moving mechanisms are modeled according to quantum–classic decision making. In our research, to model the elaboration of multi-sensory stimuli and the following decision making in terms of movement response, we adopt the D’Ariano–Faggin formalism and propose a case study with improvised dance based on a collection of poses, whose combination is presented in response to external and periodic multi-sensory stimuli. We model the dancer’s inner state and reaction to classic stimuli through a quantum circuit. We present our preliminary results, discussing further lines of development.