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Learning Control Sets for Lattice Planners from User Preferences

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Algorithmic Foundations of Robotics XIV (WAFR 2020)

Part of the book series: Springer Proceedings in Advanced Robotics ((SPAR,volume 17))

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Abstract

This work investigates the design of a motion planner that can capture user preferences. In detail, we generate a sparse control set for a lattice planner which closely follows the preferences of a user. Given a set of demonstrated trajectories from a single user, we estimate user preferences based on a weighted sum of trajectory features. We then optimize a set of connections in the lattice of given size for the user cost function. The restricted number of connections limits the branching factor, ensuring strong performance during subsequent motion planning. Further, every trajectory in the control set reflects the learned user preference while the sub-optimality due to the size restriction is minimized. We show that this problem is optimally solved by applying a separation principle: First, we find the best estimate of the user cost function given the data, then an optimal control set is computed given that estimate. We evaluate our work in a simulation for an autonomous robot in a four-dimensional spatiotemporal lattice and show that the proposed approach is suitable to replicate the demonstrated behaviour while enjoying substantially increased performance.

A. Botros and N. Wilde—Contributed equally.

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Acknowledgements

This research is partially supported by the Natural Sciences and Engineering Research Council of Canada (NSERC).

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, ON, Canada

    Alexander Botros, Nils Wilde & Stephen L. Smith

Authors
  1. Alexander Botros
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  2. Nils Wilde
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  3. Stephen L. Smith
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Corresponding author

Correspondence to Nils Wilde .

Editor information

Editors and Affiliations

  1. Center for Ubiquitous Computing, University of Oulu, Oulu, Finland

    Steven M. LaValle

  2. Brendan Iribe Center for Computer Science and Engineering, University of Maryland, College Park, MD, USA

    Ming Lin

  3. Center for Ubiquitous Computing, University of Oulu, Oulu, Finland

    Timo Ojala

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

    Dylan Shell

  5. Department of Computer Science, Rutgers University, Piscataway, NJ, USA

    Jingjin Yu

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Botros, A., Wilde, N., Smith, S.L. (2021). Learning Control Sets for Lattice Planners from User Preferences. In: LaValle, S.M., Lin, M., Ojala, T., Shell, D., Yu, J. (eds) Algorithmic Foundations of Robotics XIV. WAFR 2020. Springer Proceedings in Advanced Robotics, vol 17. Springer, Cham. https://doi.org/10.1007/978-3-030-66723-8_23

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