Computational understanding of learning-by-demonstration process to acquire fast whole body motions sequentially from the low speed motions
| Project/Area Number |
26280098
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| Research Category |
Grant-in-Aid for Scientific Research (B)
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| Allocation Type | Partial Multi-year Fund |
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| Section | 一般 |
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| Research Field |
Intelligent robotics
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| Research Institution | Ritsumeikan University |
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Principal Investigator |
Hyon Sang-Ho 立命館大学, 理工学部, 准教授 (30344691)
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| Co-Investigator(Kenkyū-buntansha) |
松原 崇充 奈良先端科学技術大学院大学, 情報科学研究科, 准教授 (20508056)
大塚 光雄 立命館大学, スポーツ健康科学部, 助教 (20611312)
下ノ村 和弘 立命館大学, 理工学部, 准教授 (80397679)
有木 由香 立命館大学, 理工学部, 助教 (80553239)
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| Project Period (FY) |
2014-04-01 – 2018-03-31
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| Project Status |
Completed (Fiscal Year 2017)
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| Budget Amount *help |
¥15,990,000 (Direct Cost: ¥12,300,000、Indirect Cost: ¥3,690,000)
Fiscal Year 2016: ¥2,860,000 (Direct Cost: ¥2,200,000、Indirect Cost: ¥660,000)
Fiscal Year 2015: ¥6,370,000 (Direct Cost: ¥4,900,000、Indirect Cost: ¥1,470,000)
Fiscal Year 2014: ¥6,760,000 (Direct Cost: ¥5,200,000、Indirect Cost: ¥1,560,000)
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| Keywords | ヒューマノイドロボット / 運動制御 / 運動学習 / ヒューマノイド / 見真似学習 / ロボットビジョン / 全身運動制御 / モーションキャプチャー / 運動生成 / 模倣学習 / 設計製作 |
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| Outline of Final Research Achievements |
In this project, in order to clarify the teaching method of whole body movement with high degree of difficulty, we aimed to propose a teaching algorithm by reinforcement learning and to verify it using real humanoid robots. During the research period, a new teaching algorithm based on stochastic optimum control theory called Kullback-Leibler control was devised. We have experimentally proved the effectiveness of the proposed method using a dual-arm manipulator developed in this project. We also proposed to use a shared latent space between the human demonstrator and the robot, to quickly acquire the optimal control policy in the low-dimensional space. The method was also validated in dynamic simulations on a biped humanoid robot.
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Report
(5 results)
Research Products
(18 results)
