The interaction and collaboration between humans and multiple robots represent a novel field of research known as human multi-robot systems. Adequately designed systems within this field allow teams composed of both humans and robots to work together effectively on tasks such as monitoring, exploration, and search and rescue operations. This paper presents a deep reinforcement learning-based affective workload allocation controller specifically for multi-human multi-robot teams. The proposed controller can dynamically reallocate workloads based on the performance of the operators during collaborative missions with multi-robot systems. The operators' performances are evaluated through the scores of a self-reported questionnaire (i.e., subjective measurement) and the results of a deep learning-based cognitive workload prediction algorithm that uses physiological and behavioral data (i.e., objective measurement). To evaluate the effectiveness of the proposed controller, we use a multi-human multi-robot CCTV monitoring task as an example and carry out comprehensive real-world experiments with 32 human subjects for both quantitative measurement and qualitative analysis. Our results demonstrate the performance and effectiveness of the proposed controller and highlight the importance of incorporating both subjective and objective measurements of the operators' cognitive workload as well as seeking consent for workload transitions, to enhance the performance of multi-human multi-robot teams.

翻译：人类与多机器人之间的交互与协作是一个被称为“人类多机器人系统”的新兴研究领域。在该领域中，合理设计的系统能让由人类和机器人组成的团队在监控、探索及搜索救援等任务中高效协同工作。本文提出了一种基于深度强化学习的情感化工作负载分配控制器，专为多人多机器人团队设计。该控制器能在与多机器人系统协作的任务中，根据操作员的表现动态重新分配工作负载。操作员的表现通过自我报告问卷得分（即主观测量）以及基于深度学习的认知工作负载预测算法结果（即客观测量，该算法利用生理和行为数据）进行评估。为验证所提控制器的有效性，我们以多人多机器人闭路电视监控任务为例，开展了包含32名受试者的综合真实世界实验，并进行了定量测量与定性分析。实验结果表明了所提控制器的性能和有效性，并强调了在提升多人多机器人团队表现时，融入对操作员认知负载的主客观测量以及寻求工作负载转移同意的重要性。