Encoding Reusable Multi-Robot Planning Strategies as Abstract Hypergraphs
<|reference_start|>Encoding Reusable Multi-Robot Planning Strategies as Abstract Hypergraphs: Multi-Robot Task Planning (MR-TP) is the search for a discrete-action plan a team of robots should take to complete a task. The complexity of such problems scales exponentially with the number of robots and task complexity, making them challenging for online solution. To accelerate MR-TP over a system's lifetime, this work looks at combining two recent advances: (i) Decomposable State Space Hypergraph (DaSH), a novel hypergraph-based framework to efficiently model and solve MR-TP problems; and \mbox{(ii) learning-by-abstraction,} a technique that enables automatic extraction of generalizable planning strategies from individual planning experiences for later reuse. Specifically, we wish to extend this strategy-learning technique, originally designed for single-robot planning, to benefit multi-robot planning using hypergraph-based MR-TP.<|reference_end|>
@article{elimelech2024encoding,
    title={Encoding Reusable Multi-Robot Planning Strategies as Abstract
  Hypergraphs},
    author={Khen Elimelech, James Motes, Marco Morales, Nancy M. Amato, Moshe Y.
  Vardi, and Lydia E. Kavraki},
    journal={arXiv preprint arXiv:2409.10692},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.10692},
    primaryClass={cs.RO cs.AI cs.MA}
}