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Robotics Research pp 115–123Cite as

Deep Coverage: Motion Synthesis in the Data-Driven Era

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Part of the book series: Springer Proceedings in Advanced Robotics ((SPAR,volume 10))

Abstract

Effective robotic systems must be able to produce desired motion in a sufficiently broad variety of robot states and environmental contexts. Classic control and planning methods achieve such coverage through the synthesis of model-based components. New applications and platforms, such as soft robots, present novel challenges, ranging from richer dynamical behaviors to increasingly unstructured environments. In these setups, derived models frequently fail to express important real-world subtleties. An increasingly popular approach to deal with this issue is the use of end-to-end machine learning architectures, which adapt to such complexities through a data-driven process. Unfortunately, however, data are not always available for all regions of the operational space, which complicates the extensibility of these solutions. In light of these issues, this paper proposes a reconciliation of classic motion synthesis with modern data-driven tools towards the objective of “deep coverage”. This notion utilizes the concept of composability, a feature of traditional control and planning methods, over data-derived “motion elements”, towards generalizable and scalable solutions that adapt to real-world experience.

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Author information

Authors and Affiliations

  1. Computer Science Department of Rutgers, The State University of New Jersey, New Brunswick, USA

    David A. Surovik & Kostas E. Bekris

Authors
  1. David A. Surovik
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  2. Kostas E. Bekris
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Corresponding author

Correspondence to Kostas E. Bekris .

Editor information

Editors and Affiliations

  1. Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL, USA

    Nancy M. Amato

  2. Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA

    Greg Hager

  3. Department of Computer Science and Engineering, Texas A&M University, College Station, TX, USA

    Shawna Thomas

  4. Department of Electrical Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile

    Miguel Torres-Torriti

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Surovik, D.A., Bekris, K.E. (2020). Deep Coverage: Motion Synthesis in the Data-Driven Era. In: Amato, N., Hager, G., Thomas, S., Torres-Torriti, M. (eds) Robotics Research. Springer Proceedings in Advanced Robotics, vol 10. Springer, Cham. https://doi.org/10.1007/978-3-030-28619-4_15

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