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Imitation Learning Framework using Principal Component Analysis for Humanoid Robot Motion Generation
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| Title: | Imitation Learning Framework using Principal Component Analysis for Humanoid Robot Motion Generation |
| Other Titles: | ヒューマノイドロボット動作生成のための主成分分析を用いた模倣学習フレームワーク |
| Authors: | 朴, 江1 Browse this author |
| Authors(alt): | Park, Garam1 |
| Keywords: | Machine Learning | | Imitation Learning | | Principal Component Analysis | | Evolutionary Process |
| Issue Date: | 25-Mar-2015 |
| Publisher: | Hokkaido University |
| Abstract: | This dissertation investigates the efficient imitation learning framework based on principal component analysis to generate desired and abundant motions using few human demonstrations in the workspace. This framework is inspired from human motor learning in upper body. In real life, a robot is expected to autonomously act with varied and abundant motions. Imitation learning might be an efficient approach to enable a robot to generate natural and abundant motions.The framework comprises off-line and on-line processes. In the off-line process, human demonstrations are used to develop a motion database. The database covers the workspace and includes robot properties. The evolved database has a clustered structure for efficiency. In the on-line process, a robot can generate desired motions using a real-time motion reconstruction method based on PCA in real time. However, during the motion reconstruction by few, slight movement, or noisy, it occurs the over-response problem. To prevent that, we uses Regularization method, and curve fitting method to reduce the tolerance. The performance of this method is verified through two case studies. The proposed framework is applied to the generation of reaching motions to an object on a table and a shelf. |
| Conffering University: | 北海道大学 |
| Degree Report Number: | 甲第11770号 |
| Degree Level: | 博士 |
| Degree Discipline: | 工学 |
| Examination Committee Members: | (主査) 教授 近野 敦, 教授 小笠原 悟司, 教授 五十嵐 一 |
| Degree Affiliation: | 情報科学研究科（システム情報科学専攻） |
| Type: | theses (doctoral) |
| URI: | http://hdl.handle.net/2115/58948 |
| Appears in Collections: | 学位論文 (Theses) > 博士 （工学）
課程博士 (Doctorate by way of Advanced Course) > 情報科学院(Graduate School of Information Science and Technology)
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