---
res:
  bibo_abstract:
  - 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.@eng
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Oliver
      foaf_name: Kroemer, Oliver
      foaf_surname: Kroemer
  - foaf_Person:
      foaf_givenName: Christoph
      foaf_name: Lampert, Christoph
      foaf_surname: Lampert
      foaf_workInfoHomepage: http://www.librecat.org/personId=40C20FD2-F248-11E8-B48F-1D18A9856A87
    orcid: 0000-0001-8622-7887
  - foaf_Person:
      foaf_givenName: Jan
      foaf_name: Peters, Jan
      foaf_surname: Peters
  bibo_doi: 10.1109/TRO.2011.2121130
  bibo_issue: '3'
  bibo_volume: 27
  dct_date: 2011^xs_gYear
  dct_identifier:
  - UT:000291404600015
  dct_language: eng
  dct_publisher: IEEE@
  dct_title: Learning dynamic tactile sensing with robust vision based training@
...