Tactile object recognition using deep learning and dropout

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

38 Citations (Scopus)

Abstract

Recognizing grasped objects with tactile sensors is beneficial in many situations, as other sensor information like vision is not always reliable. In this paper, we aim for multimodal object recognition by power grasping of objects with an unknown orientation and position relation to the hand. Few robots have the necessary tactile sensors to reliably recognize objects: in this study the multifingered hand of TWENDY-ONE is used, which has distributed skin sensors covering most of the hand, 6 axis F/T sensors in each fingertip, and provides information about the joint angles. Moreover, the hand is compliant. 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. Our results show a clear improvement when using a denoising autoencoder with dropout compared to traditional neural networks. Nevertheless, a higher number of layers did not prove to be beneficial.

Original languageEnglish
Title of host publication2014 IEEE-RAS International Conference on Humanoid Robots, Humanoids 2014
PublisherIEEE Computer Society
Pages1044-1050
Number of pages7
ISBN (Electronic)9781479971749
DOIs
Publication statusPublished - 2015 Feb 12
Event2014 14th IEEE-RAS International Conference on Humanoid Robots, Humanoids 2014 - Madrid, Spain
Duration: 2014 Nov 182014 Nov 20

Publication series

NameIEEE-RAS International Conference on Humanoid Robots
Volume2015-February
ISSN (Print)2164-0572
ISSN (Electronic)2164-0580

Other

Other2014 14th IEEE-RAS International Conference on Humanoid Robots, Humanoids 2014
CountrySpain
CityMadrid
Period14/11/1814/11/20

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ASJC Scopus subject areas

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

Cite this

Schmitz, A., Bansho, Y., Noda, K., Iwata, H., Ogata, T., & Sugano, S. (2015). Tactile object recognition using deep learning and dropout. In 2014 IEEE-RAS International Conference on Humanoid Robots, Humanoids 2014 (pp. 1044-1050). [7041493] (IEEE-RAS International Conference on Humanoid Robots; Vol. 2015-February). IEEE Computer Society. https://doi.org/10.1109/HUMANOIDS.2014.7041493