Deep Coverage: Motion Synthesis in the Data-Driven Era

  • David A. Surovik
  • Kostas E. BekrisEmail author
Conference paper
Part of the Springer Proceedings in Advanced Robotics book series (SPAR, volume 10)


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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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Computer Science Department of RutgersThe State University of New JerseyNew BrunswickUSA

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