K. Elimelech, J. Motes, M. Morales, N. M. Amato, M. Y. Vardi, and L. E. Kavraki, “Encoding Reusable Multi-Robot Planning Strategies as Abstract Hypergraphs,” in 40th Anniversary of the IEEE International Conference on Robotics and Automation, 2024.
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 (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.
PDF preprint: http://kavrakilab.org/publications/elimelech2024-hypergraphs.pdf