Towards Adversarial Training for Mobile Robots

  • Todd FlyrEmail author
  • Simon ParsonsEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11649)


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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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Computer Science, Graduate CenterCity University of New YorkNew York CityUSA
  2. 2.Department of InformaticsKing’s College LondonLondonUK

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