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Deep Learning Based BCI Control of a Robotic Service Assistant Using Intelligent Goal Formulation

D. Kuhner, L.D.J. Fiederer, J. Aldinger, F. Burget, M. Völker, R.T. Schirrmeister, C. Do, J. Bödecker, B. Nebel, T. Ball, W. Burgard
doi: https://doi.org/10.1101/282848
D. Kuhner
aDepartment of Computer Science, University of Freiburg, Germany
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  • For correspondence: aldinger@informatik.uni-freiburg.de
L.D.J. Fiederer
bFaculty of Medicine, University of Freiburg, Germany
cFaculty of Biology, University of Freiburg, Germany
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  • For correspondence: aldinger@informatik.uni-freiburg.de
J. Aldinger
aDepartment of Computer Science, University of Freiburg, Germany
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  • For correspondence: aldinger@informatik.uni-freiburg.de
F. Burget
aDepartment of Computer Science, University of Freiburg, Germany
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M. Völker
aDepartment of Computer Science, University of Freiburg, Germany
bFaculty of Medicine, University of Freiburg, Germany
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R.T. Schirrmeister
bFaculty of Medicine, University of Freiburg, Germany
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C. Do
aDepartment of Computer Science, University of Freiburg, Germany
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J. Bödecker
aDepartment of Computer Science, University of Freiburg, Germany
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B. Nebel
aDepartment of Computer Science, University of Freiburg, Germany
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T. Ball
bFaculty of Medicine, University of Freiburg, Germany
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W. Burgard
aDepartment of Computer Science, University of Freiburg, Germany
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Abstract

As autonomous service robots become more affordable and thus available for the general public, there is a growing need for user-friendly interfaces to control these systems. Control interfaces typically get more complicated with increasing complexity of the robotic tasks and the environment. Traditional control modalities as touch, speech or gesture commands are not necessarily suited for all users. While non-expert users can make the effort to familiarize themselves with a robotic system, paralyzed users may not be capable of controlling such systems even though they need robotic assistance most. In this paper, we present a novel framework, that allows these users to interact with a robotic service assistant in a closed-loop fashion, using only thoughts. The system is composed of several interacting components: non-invasive neuronal signal recording and co-adaptive deep learning which form the brain-computer interface (BCI), high-level task planning based on referring expressions, navigation and manipulation planning as well as environmental perception. We extensively evaluate the BCI in various tasks, determine the performance of the goal formulation user interface and investigate its intuitiveness in a user study. Furthermore, we demonstrate the applicability and robustness of the system in real world scenarios, considering fetch-and-carry tasks and tasks involving human-robot interaction. As our results show, the system is capable of adapting to frequent changes in the environment and reliably accomplishes given tasks within a reasonable amount of time. Combined with high-level planning using referring expressions and autonomous robotic systems, interesting new perspectives open up for non-invasive BCI-based human-robot interactions.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted March 26, 2018.
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Deep Learning Based BCI Control of a Robotic Service Assistant Using Intelligent Goal Formulation
D. Kuhner, L.D.J. Fiederer, J. Aldinger, F. Burget, M. Völker, R.T. Schirrmeister, C. Do, J. Bödecker, B. Nebel, T. Ball, W. Burgard
bioRxiv 282848; doi: https://doi.org/10.1101/282848
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Deep Learning Based BCI Control of a Robotic Service Assistant Using Intelligent Goal Formulation
D. Kuhner, L.D.J. Fiederer, J. Aldinger, F. Burget, M. Völker, R.T. Schirrmeister, C. Do, J. Bödecker, B. Nebel, T. Ball, W. Burgard
bioRxiv 282848; doi: https://doi.org/10.1101/282848

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