| Project/Area Number |
19H02116
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| Research Category |
Grant-in-Aid for Scientific Research (B)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Review Section |
Basic Section 20020:Robotics and intelligent system-related
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| Research Institution | Waseda University |
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Principal Investigator |
Schmitz Alexander 早稲田大学, 理工学術院, Visiting Researcher (30729455)
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| Co-Investigator(Kenkyū-buntansha) |
尾形 哲也 早稲田大学, 理工学術院, 教授 (00318768)
玉城 絵美 琉球大学, 工学部, 教授 (30515086)
Somlor Sophon 早稲田大学, 理工学術院総合研究所(理工学研究所), 次席研究員(研究院講師) (40791231)
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| Project Period (FY) |
2019-04-01 – 2022-03-31
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| Project Status |
Completed (Fiscal Year 2022)
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| Budget Amount *help |
¥17,420,000 (Direct Cost: ¥13,400,000、Indirect Cost: ¥4,020,000)
Fiscal Year 2021: ¥4,420,000 (Direct Cost: ¥3,400,000、Indirect Cost: ¥1,020,000)
Fiscal Year 2020: ¥6,240,000 (Direct Cost: ¥4,800,000、Indirect Cost: ¥1,440,000)
Fiscal Year 2019: ¥6,760,000 (Direct Cost: ¥5,200,000、Indirect Cost: ¥1,560,000)
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| Keywords | Robotics / Tactile Sensing / Machine Learning / Tactile Sensors / Tactile sensors / Manual skills / Robots |
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| Outline of Research at the Start |
Even the most advanced robots currently cannot robustly grasp and manipulate objects of unspecified shape, weight and stiffness. This proposal addresses these issues by developing advanced tactile sensors. Machine learning is used to replicate skills that previously could not be realized.
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| Outline of Final Research Achievements |
We have further developed our tactile sensors. 368 3-axis tactile sensors cover the robot hand. Furthermore, we have further developed a robot hand that allows to cover the palmar side of the robot hand nearly completely with thick skin. We have also further developed the tactile sensors for human hands, which now allow measuring the contact position in addition to the contact force on the fingertip without covering the human skin that is in contact with the object. Furthermore, we have developed sensors that are stretchable, so that they can be used for robot joints for example in the future.
Moreover, we have used the tactile sensors embedded in robot hands and grippers for tactile AI. The robot was able to perform various in-hand manipulations, detect object properties, and predict grasp stability as well as detect slip. We use, implement and improve state of the art machine learning methods such as attention mechanisms, ensemble learning and graph neural networks.
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| Academic Significance and Societal Importance of the Research Achievements |
Robots in the future are expected to work for and with humans. Tactile sensors are crucial for robots to perform human-like grasping and manipulation. Example applications would be the automation of warehouses, factories and agricultural activities.
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