Influence of Human–Computer Interaction-Based Intelligent Dancing Robot and Psychological Construct on Choreography

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Abstract

To study the influence of Artificial Intelligence (AI) on dancing robots in choreography, this paper introduces the biped-humanoid robot-imagined choreography model alongside the Psychological Space Construction (Psychological Construct) and Human–Computer Interaction (HCI). The proposed model is based on deep learning and imitating human thinking and is capable of imagining new dance elements. Finally, simulation experiments are designed to verify the model's effectiveness. Dance professionals are invited to evaluate the robot-imagined dance posture. The results show that the proposed model can vividly imitate human dancers and imagine and create new dance movements. The average basic feature retention and innovation scores of 30 new dance elements imagined on the L ₁ (head) are 7.29 and 7.64, respectively. By comparison, similar scores on 30 new elements in L ₂ (upper-body) are 7.73 and 7.40, respectively. Therefore, the proposed intelligent robot-imagined choreography model can help the dancing robot choreograph more finely and improve the choreography efficiency. The research results have significant practical value for dance teaching.

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Dexterous manipulation of the robot is an important part of realizing intelligence, but manipulators can only perform simple tasks such as sorting and packing in a structured environment. In view of the existing problem, this paper presents a state-of-the-art survey on an intelligent robot with the capability of autonomous deciding and learning. The paper first reviews the main achievements and research of the robot, which were mainly based on the breakthrough of automatic control and hardware in mechanics. With the evolution of artificial intelligence, many pieces of research have made further progresses in adaptive and robust control. The survey reveals that the latest research in deep learning and reinforcement learning has paved the way for highly complex tasks to be performed by robots. Furthermore, deep reinforcement learning, imitation learning, and transfer learning in robot control are discussed in detail. Finally, major achievements based on these methods are summarized and analyzed thoroughly, and future research challenges are proposed.

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Author and article information

Contributors

Liu Yang: URI : http://loop.frontiersin.org/people/1567250/overview

Journal

Journal ID (nlm-ta): Front Neurorobot

Journal ID (iso-abbrev): Front Neurorobot

Journal ID (publisher-id): Front. Neurorobot.

Title: Frontiers in Neurorobotics

Publisher: Frontiers Media S.A.

ISSN (Electronic): 1662-5218

Publication date (Electronic): 18 May 2022

Publication date Collection: 2022

Volume: 16

Electronic Location Identifier: 819550

Affiliations

[1] ¹College of Arts, Hunan University of Arts and Sciences , Changde, China

[2] ²Faculty of Music and Performing Arts, Sultan Idris Education University , Tanjung Malin, Malaysia

Author notes

Edited by: Mu-Yen Chen, National Cheng Kung University, Taiwan

Reviewed by: Hsin-Te Wu, National Ilan University, Taiwan; Jia-Lang Xu, National Chung Hsing University, Taiwan; Chi Zhong, Kyung Hee University, South Korea; Stanislav Makowski, The City University of Warsaw, Poland

*Correspondence: Liu Yang 248169016@ 123456qq.com

Article

DOI: 10.3389/fnbot.2022.819550

PMC ID: 9159471

SO-VID: a2d3e708-4f48-4d6f-96a2-25880faed1c5

License:

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

History

Date received : 21 November 2021

Date accepted : 11 April 2022

Page count

Figures: 13, Tables: 3, Equations: 3, References: 24, Pages: 12, Words: 6004

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