Towards Deep Learning Based Robot Automatic Choreography System

Authors Organisations
Type Conference Proceeding (Non-Journal item)
Original languageEnglish
Title of host publicationIntelligent Robotics and Applications
Subtitle of host publication12th International Conference, ICIRA 2019, Shenyang, China, August 8–11, 2019, Proceedings, Part IV
EditorsHaibin Yu, Jinguo Liu, Lianqing Liu, Zhaojie Ju, Yuwang Liu, Dalin Zhou
PublisherSpringer Nature
Pages629-640
Number of pages12
ISBN (Electronic)978-3-030-27538-9
ISBN (Print)978-3-030-27537-2
DOI
Publication statusE-pub ahead of print - 03 Aug 2019
Event12th International Conference on Intelligent Robotics and Applications - Shenyang, China
Duration: 08 Aug 201911 Aug 2019

Publication series

NameLecture Notes in Computer Science
Volume11743
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference12th International Conference on Intelligent Robotics and Applications
Abbreviated titleICIRA
CountryChina
CityShenyang
Period08 Aug 201911 Aug 2019
View graph of relations
Citation formats

Abstract

It is a challenge task to enable a robot to dance according to different types of music. However, two problems have not been well resolved yet: (1) how to assign a dance to a certain type of music, and (2) how to ensure a dancing robot to keep in balance. To tackle these challenges, a robot automatic choreography system based on the deep learning technology is introduced in this paper. First, two deep learning neural network models are built to convert local and global features of music to corresponding features of dance, respectively. Then, an action graph is built based on the collected dance segments; the main function of the action graph is to generate a complete dance sequence based on the dance features generated by the two deep learning models. Finally, the generated dance sequence is performed by a humanoid robot. The experimental results shows that, according to the input music, the proposed model can successfully generate dance sequences that match the input music; also, the robot can maintain its balance while it is dancing. In addition, compared with the dance sequences in the training dataset, the dance sequences generated by the model has reached the level of artificial choreography in both diversity and innovation. Therefore, this method provides a promising solution for robotic choreography automation and design assistance

Keywords

  • artificial intelligence, fuzzy sets, genetic algorithms, Human-Computer Interaction (HCI), image processing, image reconstruction, image segmentation, learning algorithms, manipulators, mobile robots, motion control, motion planning, neural networks, path planning, robotics, robots, sensors, signal processing, software engineering, wireless telecommunication systems