We introduce a new approach to address the task allocation problem in a
system of heterogeneous robots comprising of Unmanned Ground Vehicles (UGVs)
and Unmanned Aerial Vehicles (UAVs). The proposed model, \texttt{\method}, or
\textbf{G}raph \textbf{A}ttention \textbf{T}ask \textbf{A}llocato\textbf{R}
aggregates information from neighbors in the multi-robot system, with the aim
of achieving joint optimality in the target localization efficiency.Being
decentralized, our method is highly robust and adaptable to situations where
collaborators may change over time, ensuring the continuity of the mission. We
also proposed heterogeneity-aware preprocessing to let all the different types
of robots collaborate with a uniform model.The experimental results demonstrate
the effectiveness and scalability of the proposed approach in a range of
simulated scenarios. The model can allocate targets' positions close to the
expert algorithm's result, with a median spatial gap less than a unit length.
This approach can be used in multi-robot systems deployed in search and rescue
missions, environmental monitoring, and disaster response