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

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish
Title of host publication2018 IEEE-RAS 18th International Conference on Humanoid Robots, Humanoids 2018
PublisherIEEE Computer Society
Pages768-775
Number of pages8
ISBN (Electronic)9781538672839
DOIs
Publication statusPublished - 2019 Jan 23
Event18th IEEE-RAS International Conference on Humanoid Robots, Humanoids 2018 - Beijing, China
Duration: 2018 Nov 62018 Nov 9

Publication series

NameIEEE-RAS International Conference on Humanoid Robots
Volume2018-November
ISSN (Print)2164-0572
ISSN (Electronic)2164-0580

Conference

Conference18th IEEE-RAS International Conference on Humanoid Robots, Humanoids 2018
CountryChina
CityBeijing
Period18/11/618/11/9

ASJC Scopus subject areas

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

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  • Cite this

    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. In 2018 IEEE-RAS 18th International Conference on Humanoid Robots, Humanoids 2018 (pp. 768-775). [8624961] (IEEE-RAS International Conference on Humanoid Robots; Vol. 2018-November). IEEE Computer Society. https://doi.org/10.1109/HUMANOIDS.2018.8624961