  Multi-human multi-robot teams (MH-MR) obtain tremendous potential in tackling
intricate and massive missions by merging distinct strengths and expertise of
individual members. The inherent heterogeneity of these teams necessitates
advanced initial task assignment (ITA) methods that align tasks with the
intrinsic capabilities of team members from the outset. While existing
reinforcement learning approaches show encouraging results, they might fall
short in addressing the nuances of long-horizon ITA problems, particularly in
settings with large-scale MH-MR teams or multifaceted tasks. To bridge this
gap, we propose an attention-enhanced hierarchical reinforcement learning
approach that decomposes the complex ITA problem into structured sub-problems,
facilitating more efficient allocations. To bolster sub-policy learning, we
introduce a hierarchical cross-attribute attention (HCA) mechanism, encouraging
each sub-policy within the hierarchy to discern and leverage the specific
nuances in the state space that are crucial for its respective decision-making
phase. Through an extensive environmental surveillance case study, we
demonstrate the benefits of our model and the HCA inside.
