| 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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| 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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