Towards Deep Learning Based Robot Automatic Choreography System
Authors
Organisations
Type | Conference Proceeding (Non-Journal item) |
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Original language | English |
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Title of host publication | Intelligent Robotics and Applications |
Subtitle of host publication | 12th International Conference, ICIRA 2019, Shenyang, China, August 8–11, 2019, Proceedings, Part IV |
Editors | Haibin Yu, Jinguo Liu, Lianqing Liu, Zhaojie Ju, Yuwang Liu, Dalin Zhou |
Publisher | Springer Nature |
Pages | 629-640 |
Number of pages | 12 |
ISBN (Electronic) | 978-3-030-27538-9 |
ISBN (Print) | 978-3-030-27537-2 |
DOI | |
Publication status | E-pub ahead of print - 03 Aug 2019 |
Event | 12th International Conference on Intelligent Robotics and Applications - Shenyang, China Duration: 08 Aug 2019 → 11 Aug 2019 |
Publication series
Name | Lecture Notes in Computer Science |
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Volume | 11743 |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Conference
Conference | 12th International Conference on Intelligent Robotics and Applications |
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Abbreviated title | ICIRA |
Country/Territory | China |
City | Shenyang |
Period | 08 Aug 2019 → 11 Aug 2019 |
Permanent link | Permanent link |
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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