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Multi-human multi-robot teams (MH-MR) obtain tremendous potential in tacklingintricate and massive missions by merging distinct strengths and expertise ofindividual members. The inherent heterogeneity of these teams necessitatesadvanced initial task assignment (ITA) methods that align tasks with theintrinsic capabilities of team members from the outset. While existingreinforcement learning approaches show encouraging results, they might fallshort in addressing the nuances of long-horizon ITA problems, particularly insettings with large-scale MH-MR teams or multifaceted tasks. To bridge thisgap, we propose an attention-enhanced hierarchical reinforcement learningapproach that decomposes the complex ITA problem into structured sub-problems,facilitating more efficient allocations. To bolster sub-policy learning, weintroduce a hierarchical cross-attribute attention (HCA) mechanism, encouragingeach sub-policy within the hierarchy to discern and leverage the specificnuances in the state space that are crucial for its respective decision-makingphase. Through an extensive environmental surveillance case study, wedemonstrate the benefits of our model and the HCA inside.

Initial Task Assignment in Multi-Human Multi-Robot Teams: An Attention-enhanced Hierarchical Reinforcement Learning Approach