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Turing learning with hybrid discriminators: combining the best of active and passive learning

Published: 08 July 2020 Publication History

Abstract

We propose a hybrid formulation of Turing Learning and study its application in mobile robotics. Instead of using a single type of discriminator, in the hybrid formulation, both active and passive discriminators are used. Active discriminators come to their judgments while interacting with the system under investigation, which helps improve model accuracy. Passive discriminators come to their judgments while only observing the system, allowing the reuse of data samples, which for real robots would be costly to obtain. To validate these ideas, we present a case study where a simulated embodied robot is required to calibrate its distance sensor through a process of self-modeling, and without metric information of where it resides within the environment. The results show that the hybrid formulation achieves a good level of accuracy with significantly fewer data samples from the robot. The findings suggest that the self-modeling process could be realized on a mobile physical robot with a limited time and energy budget.

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Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2014. Generative adversarial nets. In Advances in Neural Information Processing Systems. MIT Press, MA, USA, 2672--2680.
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Roderich Groß, Yue Gu, Wei Li, and Melvin Gauci. 2017. Generalizing GANs: A Turing perspective. In Advances in Neural Information Processing Systems. MIT Press, MA, USA, 6316--6326.
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Wei Li, Melvin Gauci, and Roderich Groß. 2013. A coevolutionary approach to learn animal behavior through controlled interaction. In Proceedings of the 15th Annual Conference on Genetic and Evolutionary Computation. ACM, NY, USA, 223--230.
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Wei Li, Melvin Gauci, and Roderich Groß. 2016. Turing learning: A metric-free approach to inferring behavior and its application to swarms. Swarm Intelligence 10, 3 (2016), 211--243.
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Alan M Turing. 1980. Computing machinery and intelligence. Creative Computing 6, 1 (1980), 44--53.

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  1. Turing learning with hybrid discriminators: combining the best of active and passive learning

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    cover image ACM Conferences
    GECCO '20: Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion
    July 2020
    1982 pages
    ISBN:9781450371278
    DOI:10.1145/3377929
    Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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    Published: 08 July 2020

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    Author Tags

    1. active learning
    2. generative adversarial networks
    3. robotics
    4. sensor calibration
    5. turing learning

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