Learning generative models for stylistic whole-body motions and its online adaptation
| Project/Area Number |
22700177
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| Research Category |
Grant-in-Aid for Young Scientists (B)
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| Allocation Type | Single-year Grants |
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| Research Field |
Perception information processing/Intelligent robotics
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| Research Institution | Nara Institute of Science and Technology |
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Principal Investigator |
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| Project Period (FY) |
2010 – 2012
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| Project Status |
Completed (Fiscal Year 2012)
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| Budget Amount *help |
¥3,900,000 (Direct Cost: ¥3,000,000、Indirect Cost: ¥900,000)
Fiscal Year 2012: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2011: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
Fiscal Year 2010: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
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| Keywords | 全身運動 / 生成モデル学習 / 個性 / スタイル / モーションキャプチャ / EMG / 個人適応 / 次元削減 / 動的システム |
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| Research Abstract |
In this research project, we developed novel algorithms for learning generative models of a whole-body human motions, that can explicitly capture the style of the motion sequence, and for estimating both state and style variables of the model from non-stationary unlabeled sequential observations. The applicability and effectiveness of the developed algorithms were demonstrated through several applications such as real-time prediction of motion capture data, imitation learning of motor skills by humanoid robots, and learning myoelectric-robot interfaces.
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Report
(4 results)
Research Products
(32 results)
