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The Optimization Route to Robotics—and Alternatives

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Abstract

Formulating problems rigorously in terms of optimization principles has become a dominating approach in the fields of machine learning and computer vision. However, the systems described in these fields are in some respects different to integrated, modular, and embodied systems, such as the ones we aim to build in robotics. While representing systems via optimality principles is a powerful approach, relying on it as the sole approach to robotics raises substantial challenges. In this article, we take this as a starting point to discuss which ways of representing problems should be best-suited for robotics. We argue that an adequate choice of system representation—e.g. via optimization principles—must allow us to reflect the structure of the problem domain. We discuss system design principles, such as modularity, redundancy, stability, and dynamic processes, and the degree to which they are compatible with the optimization stance or instead point to alternative paradigms in robotics research. This discussion, we hope, will bring attention to this important and often ignored system-level issue in the context of robotics research.

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Notes

  1. See [11] for a non-technical introduction.

  2. Decentralized partially observed Markov decision processes.

  3. Translating from microscopic behavior (described in terms of optimality principles) to the “effective” optimality principles that describes the macroscopic behavior is one of the most interesting and fundamental problems in science per se. Renormalization, cooper pairs, superfluidity, and similar macroscopic theories are examples of this endeavor—or of circumventing this endeavor [10].

  4. Of course, we could define objective functions over a solution path as well; now desribing desirable optimization processes in terms of optimality principles.

  5. It should be noted that for the optimization approach the result is always the end point of the generated trajectory, while for the process approach either the end point or the trajectory itself can be the result.

  6. The “generative models” in statistics focus on the parametric specification of a probability density and do not particularly emphasize any process aspect.

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Authors and Affiliations

  1. Machine Learning and Robotics Lab, U Stuttgart, Stuttgart, Germany

    Marc Toussaint

  2. Neuroinformatics Group, U Bielefeld, Bielefeld, Germany

    Helge Ritter

  3. Robotics and Biology Laboratory, TU Berlin, Berlin, Germany

    Oliver Brock

Authors
  1. Marc Toussaint
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  2. Helge Ritter
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  3. Oliver Brock
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Corresponding author

Correspondence to Marc Toussaint.

Additional information

We thank the German Research Foundation for the creation of the Priority Programme SPP 1527, by which this research is supported.

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Toussaint, M., Ritter, H. & Brock, O. The Optimization Route to Robotics—and Alternatives. Künstl Intell 29, 379–388 (2015). https://doi.org/10.1007/s13218-015-0379-7

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  • DOI: https://doi.org/10.1007/s13218-015-0379-7

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