2013 Fiscal Year Annual Research Report
| Project/Area Number |
11F01759
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| Research Institution | Waseda University |
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Principal Investigator |
菅野 重樹 早稲田大学, 理工学術院, 教授
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| Co-Investigator(Kenkyū-buntansha) |
SCHMITZ Alexander 早稲田大学, 理工学術院, 外国人特別研究員
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| Keywords | ヒューマノイドロボット / 触覚 / 巧緻性 / 把持 / マニピュレーション / コンプライアンス / 学習 / 予測モデル |
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| Research Abstract |
As we have pointed out in "Work package 3 : Tactile object recognition" in the application for the JSPS postdoctoral fellowship, while interesting work on object recognition through tactile sensing has been presented in the past, there are limitations : usually the objects are fixated, so that they do not move during exploration, and they are oriented in a certain direction ; often grippers, and not dexterous hands are used ; if distributed pressure data is used, it comes from flat tactile arrays. The hands of TWENDY-ONE provide richer tactile data than any other robotic hand. It is equipped not only with distributed tactile skin sensors on most of the hand and 6-axis F/T sensors in each fingertip, but includes also somatic sensors such as motor angles and spring displacements. We have used that multifingered hand and normal grasping actions for tactile object recognition. The objects are allowed (and indeed expected) to move between grasping actions. When using tactile sensors, it is not clear what kinds of features are useful for object recognition. Recently, deep learning has shown promising results. Nevertheless, deep learning has rarely been used in robotics and to our best knowledge never for tactile sensing, probably because it is difficult to gather many samples with tactile sensors. We have employed deep learning techniques for tactile object recognition. The robot had to identify 20 different objects, the most challenging set ever used for tactile object recognition. Our results show a clear improvement when using a denoising autoencoder compared to traditional neural networks. We achieved a recognition rate of about 88%. This is one of the highest recognition rates reported so far for recognizing grasped objects with unknown orientation and translation relative to the hand. The results have also been submitted to the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2014 conference.
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| Current Status of Research Progress |
Current Status of Research Progress
1: Research has progressed more than it was originally planned.
Reason
We have achieved outstanding results and have introduced learning into our framework, which was missing in the past, as explained in the report last year.
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| Strategy for Future Research Activity |
Unfortunately a lack of publically available tactile datasets makes it difficult to compare different approaches. We plan to gather a bigger dataset and make it public. Moreover, we plan to use time series data for future work, which probably provides additional information. In general, we want to continue our collaboration of Japanese researchers with researchers from other parts of the world.
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