Encoding Reusable Multi-Robot Planning Strategies as Abstract Hypergraphs

TL;DR

This paper introduces a hypergraph-based framework combined with learning-by-abstraction to efficiently model, solve, and reuse multi-robot task planning strategies, addressing exponential complexity challenges.

Contribution

It extends a single-robot strategy-learning technique to multi-robot planning using hypergraph models, enabling reusable and scalable planning strategies.

Findings

Hypergraph-based modeling improves planning efficiency.

Learning strategies are generalizable across tasks.

Reused strategies accelerate multi-robot planning.

Abstract

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.