| 研究課題/領域番号 |
19H02116
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| 研究機関 | 早稲田大学 |
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研究代表者 |
シュミッツ アレクサンダー 早稲田大学, 理工学術院, 准教授(任期付) (30729455)
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| 研究分担者 |
尾形 哲也 早稲田大学, 理工学術院, 教授 (00318768)
玉城 絵美 早稲田大学, 理工学術院, 准教授(任期付) (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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| 研究期間 (年度) |
2019-04-01 – 2022-03-31
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| キーワード | Tactile Sensing |
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| 研究実績の概要 |
In this research we develop a smart sensing system that enables robot hands to achieve human-like manipulation skills. Key components are 1. dense 3-axis tactile sensors for robot hands and 2. learning algorithms exploiting massive 3-axis tactile data for intelligent force control.
We integrated the tactile skin sensors in grippers and robot hands. Using a novel joint (with a remote center of motion mechanism) we could achieve full coverage of the palmar side of the fingers with sensors in one gripper. Furthermore, we instrumented human hands with the sensors, to enable skill transfer from human to robot hands in the future. We used the skin sensors integrated in the robot hands for various machine learning experiments. In particular, we used deep convolutional neural networks for tactile object recognition as well as for in-hand manipulation.
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| 現在までの達成度 (区分) |
現在までの達成度 (区分)
1: 当初の計画以上に進展している
理由
Overall our progress this year was better than expected. We already performed more machine learning experiments than expected.
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| 今後の研究の推進方策 |
We will further work on the integration of the sensors into various robot hands and arms. We will continue to use the sensors for various machine learning experiments. We are dealing with an unprecedented amount of force vector measurements. Convolutional deep neural networks are the state of the art for visual data and we have shown that they can be used for tactile data as well. Indeed, the matrix-like 3D force data resembles RGB 3-dimensional visual data. We will continue to use such machine learning methods.
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