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Forward Chaining Hierarchical Partial-Order Planning

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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

In recent years, the surge in interest in integrated Task and Motion Planning (TAMP) has caused a resurgence in Task Planning research in the robotics community. Meanwhile, in the broader AI community, combining partial-order planning with grounded forward chaining has become popular for temporal task planning, preserving the ability of partial-order planners to deal with concurrent actions while exploiting the speed of modern-day grounded forward search. In this paper, we present two new planning algorithms. The first, FCPOP, combines full utilization of the delayed action ordering commitment of partial-order planning with grounded forward search guided by a temporally informed heuristic. This results in a planner that is flexible in how it structures plans with respect to action parallelism, creates high-quality plans with low makespans, and computes plans quickly. FCPOP is shown empirically to outperform state-of-the-art temporal planners on several benchmark planning problems. The second planning algorithm FCHPOP introduces hierarchical information in the form of abstract actions. This reduces the number of nodes that need to be explored and speeds up the planning process while still generating quality plans.

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Authors and Affiliations

  1. Georgia Institute of Technology, Atlanta, GA, USA

    Andrew Messing & Seth Hutchinson

Authors
  1. Andrew Messing
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  2. Seth Hutchinson
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Corresponding author

Correspondence to Andrew Messing .

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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Messing, A., Hutchinson, S. (2021). Forward Chaining Hierarchical Partial-Order Planning. 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_22

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