---
_id: '3382'
abstract:
- lang: eng
  text: Dynamic tactile sensing is a fundamental ability to recognize materials and
    objects. However, while humans are born with partially developed dynamic tactile
    sensing and quickly master this skill, today's robots remain in their infancy.
    The development of such a sense requires not only better sensors but the right
    algorithms to deal with these sensors' data as well. For example, when classifying
    a material based on touch, the data are noisy, high-dimensional, and contain irrelevant
    signals as well as essential ones. Few classification methods from machine learning
    can deal with such problems. In this paper, we propose an efficient approach to
    infer suitable lower dimensional representations of the tactile data. In order
    to classify materials based on only the sense of touch, these representations
    are autonomously discovered using visual information of the surfaces during training.
    However, accurately pairing vision and tactile samples in real-robot applications
    is a difficult problem. The proposed approach, therefore, works with weak pairings
    between the modalities. Experiments show that the resulting approach is very robust
    and yields significantly higher classification performance based on only dynamic
    tactile sensing.
article_processing_charge: No
author:
- first_name: Oliver
  full_name: Kroemer, Oliver
  last_name: Kroemer
- first_name: Christoph
  full_name: Lampert, Christoph
  id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
  last_name: Lampert
  orcid: 0000-0001-8622-7887
- first_name: Jan
  full_name: Peters, Jan
  last_name: Peters
citation:
  ama: Kroemer O, Lampert C, Peters J. Learning dynamic tactile sensing with robust
    vision based training. <i>IEEE Transactions on Robotics</i>. 2011;27(3):545-557.
    doi:<a href="https://doi.org/10.1109/TRO.2011.2121130">10.1109/TRO.2011.2121130</a>
  apa: Kroemer, O., Lampert, C., &#38; Peters, J. (2011). Learning dynamic tactile
    sensing with robust vision based training. <i>IEEE Transactions on Robotics</i>.
    IEEE. <a href="https://doi.org/10.1109/TRO.2011.2121130">https://doi.org/10.1109/TRO.2011.2121130</a>
  chicago: Kroemer, Oliver, Christoph Lampert, and Jan Peters. “Learning Dynamic Tactile
    Sensing with Robust Vision Based Training.” <i>IEEE Transactions on Robotics</i>.
    IEEE, 2011. <a href="https://doi.org/10.1109/TRO.2011.2121130">https://doi.org/10.1109/TRO.2011.2121130</a>.
  ieee: O. Kroemer, C. Lampert, and J. Peters, “Learning dynamic tactile sensing with
    robust vision based training,” <i>IEEE Transactions on Robotics</i>, vol. 27,
    no. 3. IEEE, pp. 545–557, 2011.
  ista: Kroemer O, Lampert C, Peters J. 2011. Learning dynamic tactile sensing with
    robust vision based training. IEEE Transactions on Robotics. 27(3), 545–557.
  mla: Kroemer, Oliver, et al. “Learning Dynamic Tactile Sensing with Robust Vision
    Based Training.” <i>IEEE Transactions on Robotics</i>, vol. 27, no. 3, IEEE, 2011,
    pp. 545–57, doi:<a href="https://doi.org/10.1109/TRO.2011.2121130">10.1109/TRO.2011.2121130</a>.
  short: O. Kroemer, C. Lampert, J. Peters, IEEE Transactions on Robotics 27 (2011)
    545–557.
date_created: 2018-12-11T12:03:01Z
date_published: 2011-05-21T00:00:00Z
date_updated: 2025-09-30T08:48:31Z
day: '21'
department:
- _id: ChLa
doi: 10.1109/TRO.2011.2121130
external_id:
  isi:
  - '000291404600015'
intvolume: '        27'
isi: 1
issue: '3'
language:
- iso: eng
month: '05'
oa_version: None
page: 545 - 557
publication: IEEE Transactions on Robotics
publication_status: published
publisher: IEEE
publist_id: '3225'
quality_controlled: '1'
scopus_import: '1'
status: public
title: Learning dynamic tactile sensing with robust vision based training
type: journal_article
user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345
volume: 27
year: '2011'
...