| Budget Amount *help |
¥4,030,000 (Direct Cost: ¥3,100,000、Indirect Cost: ¥930,000)
Fiscal Year 2017: ¥780,000 (Direct Cost: ¥600,000、Indirect Cost: ¥180,000)
Fiscal Year 2016: ¥1,820,000 (Direct Cost: ¥1,400,000、Indirect Cost: ¥420,000)
Fiscal Year 2015: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
|
|---|
| Outline of Final Research Achievements |
In this study, we aim to develop a learning method of non-verbal knowledge such as motion and multimodal dialogue with a robot using a cloud robotics platform. Time series prediction method based on Deep Neural Network which introduced Dynamic Pre-training was proposed and its effectiveness was validated by using a standard motion data. In addition to improving the cloud robotics platform Rospeex, we have built a domestic service robot that has over 10,000 multimodal concepts. We also developed a multimodal language understanding method Latent Classifier GAN (LAC-GAN) that can understand user commands according to the situation.
|
