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  Learning Dynamic Tactile Sensing with Robust Vision-based Training

Kroemer, O., Lampert, C., & Peters, J. (2011). Learning Dynamic Tactile Sensing with Robust Vision-based Training. IEEE Transactions on Robotics, 27(3), 545-557. doi:10.1109/TRO.2011.2121130.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-BB5C-F Version Permalink: http://hdl.handle.net/21.11116/0000-0001-B592-D
Genre: Journal Article

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 Creators:
Kroemer, O1, 2, Author              
Lampert, CH, Author              
Peters, J1, 2, Author              
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 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.

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 Dates: 2011-06
 Publication Status: Published in print
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 Rev. Method: -
 Identifiers: DOI: 10.1109/TRO.2011.2121130
BibTex Citekey: 7054
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Title: IEEE Transactions on Robotics
  Other : IEEE Transactions on Robotics and Automation
Source Genre: Journal
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Publ. Info: New York, NY : IEEE
Pages: - Volume / Issue: 27 (3) Sequence Number: - Start / End Page: 545 - 557 Identifier: ISSN: 1042-296X
CoNE: https://pure.mpg.de/cone/journals/resource/111099121186002