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 formal analysis and taxonomy of task allocation in multi-robot systems,
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.RO 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
Derives AoI lower bounds separating sensing and propagation terms for multi-robot systems on graphs, solves sensing allocation optimally via greedy water-filling, and constructs a shortest-path-tree conveyor architecture proven to attain the bound under full-conveyor conditions.
citing papers explorer
-
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.
-
AoI-Aware Multi-Robot Sensing and Transport on Connected Graphs
Derives AoI lower bounds separating sensing and propagation terms for multi-robot systems on graphs, solves sensing allocation optimally via greedy water-filling, and constructs a shortest-path-tree conveyor architecture proven to attain the bound under full-conveyor conditions.