Learning Motions and Responses from Large-Scale Data on a Cloud-Robotics Platform
| Project/Area Number |
15K16074
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| Research Category |
Grant-in-Aid for Young Scientists (B)
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| Allocation Type | Multi-year Fund |
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| Research Field |
Intelligent robotics
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| Research Institution | National Institute of Information and Communications Technology |
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Principal Investigator |
Sugiura Komei 国立研究開発法人情報通信研究機構, 先進的音声翻訳研究開発推進センター先進的音声技術研究室, 主任研究員 (60470473)
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| Project Period (FY) |
2015-04-01 – 2018-03-31
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| Project Status |
Completed (Fiscal Year 2017)
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| Budget Amount *help |
¥4,030,000 (Direct Cost: ¥3,100,000、Indirect Cost: ¥930,000)
Fiscal Year 2017: ¥780,000 (Direct Cost: ¥600,000、Indirect Cost: ¥180,000)
Fiscal Year 2016: ¥1,820,000 (Direct Cost: ¥1,400,000、Indirect Cost: ¥420,000)
Fiscal Year 2015: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
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| Keywords | 知能ロボティクス / マルチモーダル言語理解 / クラウドロボティクス / 模倣学習 / 機械学習 / 動作認識 / ヒューマンロボットインタラクション |
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| Outline of Final Research Achievements |
In this study, we aim to develop a learning method of non-verbal knowledge such as motion and multimodal dialogue with a robot using a cloud robotics platform. Time series prediction method based on Deep Neural Network which introduced Dynamic Pre-training was proposed and its effectiveness was validated by using a standard motion data. In addition to improving the cloud robotics platform Rospeex, we have built a domestic service robot that has over 10,000 multimodal concepts. We also developed a multimodal language understanding method Latent Classifier GAN (LAC-GAN) that can understand user commands according to the situation.
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Report
(4 results)
Research Products
(21 results)
