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Conference on Biomimetic and Biohybrid Systems

Living Machines 2020: Biomimetic and Biohybrid Systems pp 128–140Cite as

Haptic Object Identification for Advanced Manipulation Skills

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12413))

Abstract

In order to identify the characteristics of unknown objects, humans - in contrast to robotic systems - are experts in exploiting their sensory and motoric abilities to refine visual information via haptic perception. While recent research has focused on either estimating the geometry or material properties, this work strives to combine these aspects by outlining a probabilistic framework that efficiently refines initial knowledge from visual sensors by generating a belief state over the object shape while simultaneously learn material parameters. Specifically, we present a grid-based and a shape-based exploration strategy, that both apply the concepts of Bayesian-Filter theory in order to decrease the uncertainty. Furthermore, the presented framework is able to learn about the geometry as well as to distinguish areas of different material types by applying unsupervised machine learning methods. The experimental results from a virtual exploration task highlight the potential of the presented methods towards enabling robots to autonomously explore unknown objects, yielding information about shape and structure of the underlying object and thus, opening doors to robotic applications where environmental knowledge is limited.

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Notes

  1. 1.

    The framework outlined in this work is not restricted to the presented identification method. Nonetheless, this specific example serves as a proof of concept.

  2. 2.

    We refer to [20] for detailed information.

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Acknowledgement

The research leading to these results has received funding from the Horizon 2020 research and innovation programme under grant agreement №820742 of the project “HR-Recycler - Hybrid Human-Robot RECYcling plant for electriCal and eLEctRonic equipment”.

Author information

Authors and Affiliations

  1. Chair of Automatic Control Engineering, Munich, Germany

    Volker Gabler, Korbinian Maier & Dirk Wollherr

  2. Chair of Information Oriented Control, Department of Electrical and Computer Engineering, Technical University of Munich, Munich, Germany

    Satoshi Endo

Authors
  1. Volker Gabler
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  2. Korbinian Maier
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  3. Satoshi Endo
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  4. Dirk Wollherr
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Corresponding author

Correspondence to Volker Gabler .

Editor information

Editors and Affiliations

  1. SPECS, Institute for Bioengineering of Catalonia, Barcelona, Spain

    Vasiliki Vouloutsi

  2. SPECS, Institute for Bioengineering of Catalonia, Barcelona, Spain

    Anna Mura

  3. University of Freiburg, Freiburg, Germany

    Falk Tauber

  4. University of Freiburg, Freiburg, Germany

    Thomas Speck

  5. University of Sheffield, Sheffield, UK

    Tony J. Prescott

  6. SPECS, Institute for Bioengineering of Catalonia, Barcelona, Spain

    Paul F. M. J. Verschure

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Gabler, V., Maier, K., Endo, S., Wollherr, D. (2020). Haptic Object Identification for Advanced Manipulation Skills. In: Vouloutsi, V., Mura, A., Tauber, F., Speck, T., Prescott, T.J., Verschure, P.F.M.J. (eds) Biomimetic and Biohybrid Systems. Living Machines 2020. Lecture Notes in Computer Science(), vol 12413. Springer, Cham. https://doi.org/10.1007/978-3-030-64313-3_14

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  • DOI: https://doi.org/10.1007/978-3-030-64313-3_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-64312-6

  • Online ISBN: 978-3-030-64313-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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