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Authors: Todd W. Flyr 1 and Simon Parsons 1 ; 2

Affiliations: 1 Department of Computer Science, Graduate Center, City University of New York, New York, U.S.A. ; 2 School of Computer Science, University of Lincoln, Lincoln, U.K.

Keyword(s): Mobile Robotics, GANs, Adversarial Training, Machine Learning.

Abstract: This paper reports research training a mobile robot to carry out a simple task. Specifically, we report on experiments in learning to strike a ball to hit a target on the ground. We trained a neural network to control a robot to carry out this task with data from a small number of trials with a physical robot. We compare the results of using this neural network with that of using a neural-network trained with this dataset plus the output of a generative adversarial network (GAN) trained on the same data. We find that the neural network that uses the GAN-generated data provides better performance. This relationship holds as we present the robot with generalized versions of this task. We also find that we can produce acceptable results with an exceptionally small initial dataset. We propose that this is a possible way to solve the “big data” problem, where training a neural network to learn physical tasks requires a large corpus of labeled trial data that can be difficult to obtain.

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Paper citation in several formats:
Flyr, T. and Parsons, S. (2020). Improving Learning in a Mobile Robot using Adversarial Training. In Proceedings of the International Conference on Robotics, Computer Vision and Intelligent Systems - ROBOVIS; ISBN 978-989-758-479-4, SciTePress, pages 82-89. DOI: 10.5220/0010107100820089

@conference{robovis20,
author={Todd W. Flyr. and Simon Parsons.},
title={Improving Learning in a Mobile Robot using Adversarial Training},
booktitle={Proceedings of the International Conference on Robotics, Computer Vision and Intelligent Systems - ROBOVIS},
year={2020},
pages={82-89},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010107100820089},
isbn={978-989-758-479-4},
}

TY - CONF

JO - Proceedings of the International Conference on Robotics, Computer Vision and Intelligent Systems - ROBOVIS
TI - Improving Learning in a Mobile Robot using Adversarial Training
SN - 978-989-758-479-4
AU - Flyr, T.
AU - Parsons, S.
PY - 2020
SP - 82
EP - 89
DO - 10.5220/0010107100820089
PB - SciTePress

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