Creation and cognition for humanoid live dancing

Augello, Agnese; Infantino, Ignazio; Manfrè, Adriano; Pilato, Giovanni; Vella, Filippo; Chella, Antonio

doi:10.1016/j.robot.2016.09.012

Cited by 25 publications

(16 citation statements)

References 8 publications

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“…In this section, we deepen the explanation of what we intend with these typologies of creativity, that we outlined in section 2, and, supported by the results and by some examples extracted from the study carried out in the schools, we will discuss possible ways to introduce these two ways of being creative in a social agent, taking inspiration from existing approaches to computational creativity (Boden, 2004; Wiggins, 2006a,b; Augello et al, 2014, 2016b; Olteţeanu and Falomir, 2016; Kelly and Gero, 2017).…”

Section: A Discussion About the Implications Of The Study For The mentioning

confidence: 89%

Analyzing Creativity in the Light of Social Practice Theory

et al. 2019

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In this work, starting from the social practice theory, we identified two kinds of creativity: a situational creativity that takes place when, starting from a defined situation, a social practice is played; and a creativity of habit that concerns the agents' capacity for generating new practices from habit when the situation is not defined or is unexpected. To test this hypothesis, the Torrance Test of Creative Thinking (Verbal Form A) was analyzed in the light of praxeology, and the results are analyzed in a computational creativity perspective.

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Section: A Discussion About the Implications Of The Study For The mentioning

confidence: 89%

Analyzing Creativity in the Light of Social Practice Theory

et al. 2019

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“…To explore live performances, Augello et al [22] presented a cognitive architecture based on human-robot interactions. The architecture integrated HMM and genetic algorithms to create and perform improvisational robotic dance according to the perceived music.…”

Section: Aesthetic Evaluation On Robotic Dance Objectmentioning

confidence: 99%

Creating a Computable Cognitive Model of Visual Aesthetics for Automatic Aesthetics Evaluation of Robotic Dance Poses

Peng

et al. 2019

Symmetry

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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.

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Section: Dancementioning

confidence: 99%

“…Just as in drawing, researchers developed a robot system that can learn dancing styles from human expert dancers. By employing an interactive version of Simple Genetic Algorithm (SGA) [40], Augello et al [41] developed a learning and evolving dancing robot. One of the interesting parts of their robot dancer is that it creates choreographies according to its artistic motivation, influenced by its urges.…”

Section: Dancementioning

confidence: 99%

Robotic Arts: Current Practices, Potentials, and Implications

Jeon

2017

MTI

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Abstract:Given that the origin of the "robot" comes from efforts to create a worker to help people, there has been relatively little research on making a robot for non-work purposes. However, some researchers have explored robotic arts since Leonardo da Vinci. Many questions can be posed regarding the potentials of robotic arts: (1) Is there anything we can call machine-creativity? (2) Can robots improvise artworks on the fly? and (3) Can art robots pass the Turing test? To ponder these questions and see the current status quo of robotic arts, the present paper surveys the contributions of robotics in diverse forms of arts, including drawing, theater, music, and dance. The present paper describes selective projects in each genre, core procedure, possibilities and limitations within the aesthetic computing framework. Then, the paper discusses implications of these robotic arts in terms of both robot research and art research, followed by conclusions including answers to the questions posed at the outset.

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Creation and cognition for humanoid live dancing

Cited by 25 publications

References 8 publications

Analyzing Creativity in the Light of Social Practice Theory

Analyzing Creativity in the Light of Social Practice Theory

Creating a Computable Cognitive Model of Visual Aesthetics for Automatic Aesthetics Evaluation of Robotic Dance Poses

Robotic Arts: Current Practices, Potentials, and Implications

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