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Controls and Art pp 1–26Cite as

Metric Preference Learning with Applications to Motion Imitation

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Abstract

In order for engineered systems to produce behaviors that achieve esthetic goals, one requires objective functions that accurately represent potentially subjective, human preferences as opposed to a priori given objectives. Starting from a collection of empirical, pairwise comparisons, we approach this issue by developing objective functions that are compatible with the expressed preferences. In addition, robust estimators for global optimizers to these functions are derived together with graph-theoretic simplification methods for the resulting systems of constraints and a limited memory asymptotic observer that finds a globally optimal alternative (e.g., motion). Two examples are presented involving the comparison of apples and oranges, and of human and synthetic motions.

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Notes

  1. 1.

    To the extent that a distinction is made between “instances” and “alternatives,” it is that “instances” are the points that were shown to human judges, whereas “alternatives” may also include other points in the space besides those that were seen.

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Acknowledgments

This work was supported by the U.S. National Science Foundation through Creative IT Grant #0757317. The human-study was conducted within the Georgia Institute of Technology, Institute Review Board Protocol H08162 - “Perceived Similarity Study.” We would like to thank Akhil Bahl for his assistance in producing the motion capture data used in the synthetic amoeba experiment.

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Authors and Affiliations

  1. School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, 30332, USA

    Peter Kingston & Magnus Egerstedt

  2. Center for Puppetry Arts, Atlanta, GA , 30309, USA

    Jason von Hinezmeyer

Authors
  1. Peter Kingston
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  2. Jason von Hinezmeyer
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  3. Magnus Egerstedt
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Corresponding author

Correspondence to Magnus Egerstedt .

Editor information

Editors and Affiliations

  1. Systems and Information Engineering Department, University of Virginia, Charlottesville, Virginia, USA

    Amy LaViers

  2. School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA

    Magnus Egerstedt

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Kingston, P., von Hinezmeyer, J., Egerstedt, M. (2014). Metric Preference Learning with Applications to Motion Imitation. In: LaViers, A., Egerstedt, M. (eds) Controls and Art. Springer, Cham. https://doi.org/10.1007/978-3-319-03904-6_1

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  • DOI: https://doi.org/10.1007/978-3-319-03904-6_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-03903-9

  • Online ISBN: 978-3-319-03904-6

  • eBook Packages: EngineeringEngineering (R0)

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