针对人机协同团队异构与信息不确定性，提出自适应任务分配框架。
Adaptive Task Allocation in Multi-Human Multi-Robot Teams under Team Heterogeneity and Dynamic Information Uncertainty
- 基于分层强化学习，结合团队异构性进行初始分配
- 动态响应状态变化，实现条件性任务重分配
- 引入辅助状态学习，缓解信息不确定性，适合复杂协作场景
多人类多机器人（MH-MR）团队中的任务分配面临成员异构性、任务执行动态性以及操作状态信息不确定性的挑战。现有方法难以同时应对这些因素，导致性能不佳。为此，本文提出ATA-HRL框架，采用分层强化学习（HRL），融合初始任务分配（ITA）以利用团队异构性，并根据动态操作状态实现条件性任务重分配。此外，引入辅助状态表示学习任务以应对信息不确定性，提升任务执行效果。通过大规模环境监测任务的案例研究，验证了该方法的有效性。
原文摘要 · Abstract (English)
Task allocation in multi-human multi-robot (MH-MR) teams presents significant challenges due to the inherent heterogeneity of team members, the dynamics of task execution, and the information uncertainty of operational states. Existing approaches often fail to address these challenges simultaneously, resulting in suboptimal performance. To tackle this, we propose ATA-HRL, an adaptive task allocation framework using hierarchical reinforcement learning (HRL), which incorporates initial task allocation (ITA) that leverages team heterogeneity and conditional task reallocation in response to dynamic operational states. Additionally, we introduce an auxiliary state representation learning task to manage information uncertainty and enhance task execution. Through an extensive case study in large-scale environmental monitoring tasks, we demonstrate the benefits of our approach.
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