A graph neural network learns to approximate altruistic robot transfers across heterogeneous teams using Hamilton's rule, achieving near-optimal allocation in simulated firefighting scenarios.
A comprehensive taxonomy for multi-robot task allocation
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M2M solves the many-to-many MAPD problem with two variants and outperforms prior one-to-one methods by completing up to 22,000 more tasks on average in 8-hour warehouse simulations.
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Learning Altruistic Collaboration in Heterogeneous Multi-Team Systems
A graph neural network learns to approximate altruistic robot transfers across heterogeneous teams using Hamilton's rule, achieving near-optimal allocation in simulated firefighting scenarios.
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Many-to-Many Multi-Agent Pickup and Delivery
M2M solves the many-to-many MAPD problem with two variants and outperforms prior one-to-one methods by completing up to 22,000 more tasks on average in 8-hour warehouse simulations.