An Empiric Evaluation of a Real-Time Robot Dancing Framework based on Multi-Modal Events

et al. 2019

Symmetry

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.

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

et al. 2019

Symmetry

“…In the creation of robotic choreography, most popular methods include random generation [6], mapping rule [7,8], chaos dynamics [9], interactive reinforcement learning [10], evolutionary computation [11][12][13][14][15][16], and the Markov chain model [17,18]. This section will briefly review these creation methods.…”

Section: Related Workmentioning

confidence: 99%

“…Oliveira et al [7] and de Sousa Junior et al [8] deployed mapping rules to create robotic choreography, which maps the environmental events (features) to some fixed robotic dance motions, and produces the autonomous robotic dance with the continuous stimuli of its embodied environment. The mapping rules can be customized and robotic dance can be generated in real time.…”

Section: Related Workmentioning

confidence: 99%

Robotic Choreography Inspired by the Method of Human Dance Creation

et al. 2018

Information

In general, human dance is created by the imagination and innovativeness of human dancers, which in turn provides an inspiration for robotic choreography generation. This paper proposes a novel mechanism for a humanoid robot to create good choreography autonomously with the imagination of human dance. Such a mechanism combines innovativeness with the characteristic preservation of human dance, and enables a humanoid robot to present the characteristics of “imitation, memory, imagination, process and combination”. The proposed mechanism has been implemented on a real humanoid robot, NAO, to verify its feasibility and performance. Experimental results are presented to demonstrate good performance of the proposed mechanism.

“…Shinozaki et al [12] constructed a robot dance system of hip-hop, and invited ten people to evaluate the generated robotic dance motions from some specific items (such as dynamic, exciting, wonder, smooth, etc.). Moreover, Oliveira et al [13] implemented a realtime robot dancing framework based on multimodal events, and each evaluator was asked to fulfill a Likert-scale questionnaire to make an empiric evaluation of robotic dance.…”

Section: Introductionmentioning

confidence: 99%

Multiple Visual Feature Integration Based Automatic Aesthetics Evaluation of Robotic Dance Motions

Wang

et al. 2021

Information

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.