Encoding Reusable Multi-Robot Planning Strategies as Abstract Hypergraphs
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866916397128351744 |
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| author | Elimelech, Khen Motes, James Morales, Marco Amato, Nancy M. Vardi, Moshe Y. Kavraki, Lydia E. |
| author_facet | Elimelech, Khen Motes, James Morales, Marco Amato, Nancy M. Vardi, Moshe Y. Kavraki, Lydia E. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_10692 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Encoding Reusable Multi-Robot Planning Strategies as Abstract Hypergraphs Elimelech, Khen Motes, James Morales, Marco Amato, Nancy M. Vardi, Moshe Y. Kavraki, Lydia E. Robotics Artificial Intelligence Multiagent Systems 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. |
| title | Encoding Reusable Multi-Robot Planning Strategies as Abstract Hypergraphs |
| topic | Robotics Artificial Intelligence Multiagent Systems |
| url | https://arxiv.org/abs/2409.10692 |