Versatile In-Hand Manipulation of Objects with Different Sizes and Shapes Using Neural Networks

Satoshi Funabashi, Alexander Schmitz, Takashi Sato, Sophon Somlor, Shigeki Sugano

研究成果: Conference contribution

抄録

Changing the grasping posture of objects within a robot hand is hard to achieve, especially if the objects are of various shape and size. In this paper we use a neural network to learn such manipulation with variously sized and shaped objects. The TWENDY-ONE hand possesses various properties that are effective for in-hand manipulation: a high number of actuated joints, passive degrees of freedom and soft skin, six-axis force/torque (F /T) sensors in each fingertip and distributed tactile sensors in the soft skin. The object size information is extracted from the initial grasping posture. The training data includes tactile and the object information. After training the neural network, the robot is able to manipulate objects of not only trained but also untrained size and shape. The results show the importance of size and tactile information. Importantly, the features extracted by a stacked autoencoder (trained with a larger dataset) could reduce the number of required training samples for supervised learning of in-hand manipulation.

元の言語English
ホスト出版物のタイトル2018 IEEE-RAS 18th International Conference on Humanoid Robots, Humanoids 2018
出版者IEEE Computer Society
ページ768-775
ページ数8
ISBN(電子版)9781538672839
DOI
出版物ステータスPublished - 2019 1 23
イベント18th IEEE-RAS International Conference on Humanoid Robots, Humanoids 2018 - Beijing, China
継続期間: 2018 11 62018 11 9

出版物シリーズ

名前IEEE-RAS International Conference on Humanoid Robots
2018-November
ISSN(印刷物)2164-0572
ISSN(電子版)2164-0580

Conference

Conference18th IEEE-RAS International Conference on Humanoid Robots, Humanoids 2018
China
Beijing
期間18/11/618/11/9

Fingerprint

Skin
Robots
Neural networks
Supervised learning
Sensors
End effectors
Torque

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Hardware and Architecture
  • Human-Computer Interaction
  • Electrical and Electronic Engineering

これを引用

Funabashi, S., Schmitz, A., Sato, T., Somlor, S., & Sugano, S. (2019). Versatile In-Hand Manipulation of Objects with Different Sizes and Shapes Using Neural Networks. : 2018 IEEE-RAS 18th International Conference on Humanoid Robots, Humanoids 2018 (pp. 768-775). [8624961] (IEEE-RAS International Conference on Humanoid Robots; 巻数 2018-November). IEEE Computer Society. https://doi.org/10.1109/HUMANOIDS.2018.8624961

Versatile In-Hand Manipulation of Objects with Different Sizes and Shapes Using Neural Networks. / Funabashi, Satoshi; Schmitz, Alexander; Sato, Takashi; Somlor, Sophon; Sugano, Shigeki.

2018 IEEE-RAS 18th International Conference on Humanoid Robots, Humanoids 2018. IEEE Computer Society, 2019. p. 768-775 8624961 (IEEE-RAS International Conference on Humanoid Robots; 巻 2018-November).

研究成果: Conference contribution

Funabashi, S, Schmitz, A, Sato, T, Somlor, S & Sugano, S 2019, Versatile In-Hand Manipulation of Objects with Different Sizes and Shapes Using Neural Networks. : 2018 IEEE-RAS 18th International Conference on Humanoid Robots, Humanoids 2018., 8624961, IEEE-RAS International Conference on Humanoid Robots, 巻. 2018-November, IEEE Computer Society, pp. 768-775, 18th IEEE-RAS International Conference on Humanoid Robots, Humanoids 2018, Beijing, China, 18/11/6. https://doi.org/10.1109/HUMANOIDS.2018.8624961
Funabashi S, Schmitz A, Sato T, Somlor S, Sugano S. Versatile In-Hand Manipulation of Objects with Different Sizes and Shapes Using Neural Networks. : 2018 IEEE-RAS 18th International Conference on Humanoid Robots, Humanoids 2018. IEEE Computer Society. 2019. p. 768-775. 8624961. (IEEE-RAS International Conference on Humanoid Robots). https://doi.org/10.1109/HUMANOIDS.2018.8624961
Funabashi, Satoshi ; Schmitz, Alexander ; Sato, Takashi ; Somlor, Sophon ; Sugano, Shigeki. / Versatile In-Hand Manipulation of Objects with Different Sizes and Shapes Using Neural Networks. 2018 IEEE-RAS 18th International Conference on Humanoid Robots, Humanoids 2018. IEEE Computer Society, 2019. pp. 768-775 (IEEE-RAS International Conference on Humanoid Robots).
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