# Encoding Reusable Multi-Robot Planning Strategies as Abstract Hypergraphs

arXiv: https://arxiv.org/abs/2409.10692 · HTML: https://mlsanity.com/paper/2409.10692/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 6 references that are in the map.
- First posted on arXiv 16 Sep 2024.

## Builds on (most cited first, 1 of 1)

- [Hypergraph-based Multi-Robot Task and Motion Planning](https://mlsanity.com/paper/2210.04333/) (2022)

## Abstract (arXiv)

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.