| Budget Amount *help |
¥42,900,000 (Direct Cost: ¥33,000,000、Indirect Cost: ¥9,900,000)
Fiscal Year 2019: ¥6,760,000 (Direct Cost: ¥5,200,000、Indirect Cost: ¥1,560,000)
Fiscal Year 2018: ¥6,760,000 (Direct Cost: ¥5,200,000、Indirect Cost: ¥1,560,000)
Fiscal Year 2017: ¥7,410,000 (Direct Cost: ¥5,700,000、Indirect Cost: ¥1,710,000)
Fiscal Year 2016: ¥8,970,000 (Direct Cost: ¥6,900,000、Indirect Cost: ¥2,070,000)
Fiscal Year 2015: ¥13,000,000 (Direct Cost: ¥10,000,000、Indirect Cost: ¥3,000,000)
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| Outline of Final Research Achievements |
In recent years, deep learning has been used in a variety of domains, but its application is limited to cyber data processing. It is not sufficiently easy to work in the real world. On the other hand, there is a strong expectation for life support using robots. In recent years, the possibility of general-purpose robots has been attracting attention. In this study, we have developed a novel robot learning model using deep learning. Also we have improved the robot performance for manipulating various materials and reduced the development cost with robot operation system, RT-Middleware.
Specifically, based on the findings of cognitive developmental robotics research which relates to infant developmental learning including imitation learning, predictive coding, etc. we developed multiple robots which are modeling the sensory-motor information (experience) by deep learning.
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