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.

翻译：多人类多机器人团队（MH-MR）通过融合个体成员的不同优势与专业知识，在应对复杂且宏大的任务中展现出巨大潜力。这类团队固有的异质性需要先进的初始任务分配（ITA）方法，从任务伊始便将其与团队成员的内在能力相匹配。尽管现有强化学习方法展现了令人鼓舞的成果，但在处理长期ITA问题的细微之处时仍显不足，尤其是在大规模MH-MR团队或多方面任务的场景中。为弥补这一缺口，我们提出一种注意力增强的分层强化学习方法，将复杂的ITA问题分解为结构化子问题，从而促进更高效的分配。为强化子策略学习，我们引入分层跨属性注意力（HCA）机制，促使层级中的每个子策略能够识别并利用状态空间中对其各自决策阶段至关重要的特定细节。通过综合环境监控案例研究，我们展示了所提模型及内部HCA机制的优势。