Creation of Robot's Tool-Body Assimilation Model Using Sparse Recurrent Neural Network
| Project/Area Number |
25730159
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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 | The University of Tokushima (2014-2015)
Kyoto University (2013) |
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Principal Investigator |
Nishide Shun 徳島大学, ソシオテクノサイエンス研究部, 講師 (30613400)
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| Project Period (FY) |
2013-04-01 – 2016-03-31
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| Project Status |
Completed (Fiscal Year 2015)
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| Budget Amount *help |
¥4,160,000 (Direct Cost: ¥3,200,000、Indirect Cost: ¥960,000)
Fiscal Year 2015: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2014: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2013: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
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| Keywords | 再帰結合神経回路モデル / ロボット / スパース結合 / 再帰神経回路モデル / 認知発達ロボティクス |
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| Outline of Final Research Achievements |
In this research, we investigated the effectiveness of sparse connections using Multiple Timescales Recurrent Neural Network (MTRNN), for applying to robot's motion learning. From experiments with nine types of human motions, sparse networks were confirmed to achieve better training results, compared to full-connected networks. As examples of motion learning, we created models for robot's self body learning and developmental imitation drawing learning model.
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Report
(4 results)
Research Products
(15 results)
