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Annual Conference Towards Autonomous Robotic Systems

TAROS 2019: Towards Autonomous Robotic Systems pp 197–208Cite as

Towards Adversarial Training for Mobile Robots

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11649))

Abstract

This paper reports some preliminary work on learning on a physical robot. In particular, we report on an experiment to learn how to strike a ball to hit a target on the ground. We compare learning based just on previous trials with the robot with learning based on those trials plus additional data learnt using a generative adversarial network (GAN). We find that the additional data generated by the GAN improves the performance of the robot.

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References

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Author information

Authors and Affiliations

  1. Department of Computer Science, Graduate Center, City University of New York, New York City, USA

    Todd Flyr

  2. Department of Informatics, King’s College London, London, UK

    Simon Parsons

Authors
  1. Todd Flyr
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  2. Simon Parsons
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Corresponding authors

Correspondence to Todd Flyr or Simon Parsons .

Editor information

Editors and Affiliations

  1. Queen Mary University of London, London, UK

    Kaspar Althoefer

  2. Queen Mary University of London, London, UK

    Jelizaveta Konstantinova

  3. Queen Mary University of London, London, UK

    Ketao Zhang

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Flyr, T., Parsons, S. (2019). Towards Adversarial Training for Mobile Robots. In: Althoefer, K., Konstantinova, J., Zhang, K. (eds) Towards Autonomous Robotic Systems. TAROS 2019. Lecture Notes in Computer Science(), vol 11649. Springer, Cham. https://doi.org/10.1007/978-3-030-23807-0_17

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  • DOI: https://doi.org/10.1007/978-3-030-23807-0_17

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-23806-3

  • Online ISBN: 978-3-030-23807-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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