This paper addresses the challenge of enabling a single robot to effectively assist multiple humans in decision-making for task planning domains. We introduce a comprehensive framework designed to enhance overall team performance by considering both human expertise in making the optimal decisions and robot influence on human decision-making. Our model integrates these factors seamlessly within the task-planning domain, formulating the problem as a partially observable Markov decision process (POMDP) while treating expertise and influence as unobservable components of the system state. To solve for the robot's actions in such systems, we propose an efficient Attention-Switching policy. This policy capitalizes on the inherent structure of such systems, solving multiple smaller POMDPs to generate heuristics for prioritizing interactions with different human teammates, thereby reducing the state space and improving scalability. Our empirical results on a simulated kit fulfillment task demonstrate improved team performance when the robot's policy accounts for both expertise and influence. This research represents a significant step forward in the field of adaptive robot assistance, paving the way for integration into cost-effective small and mid-scale industries, where substantial investments in robotic infrastructure may not be economically viable.

翻译：本文探讨了使单个机器人能够有效辅助多人在任务规划领域进行决策的挑战性问题。我们提出了一套综合框架，旨在通过同时考虑人类做出最优决策的专业性以及机器人对人类决策的影响力，来提升整体团队绩效。该模型将这两类因素无缝整合至任务规划领域，将问题建模为部分可观察马尔可夫决策过程（POMDP），并将专业性与影响力视为系统状态中不可观测的组成部分。为求解此类系统中机器人的动作策略，我们提出了一种高效的注意力切换策略。该策略利用此类系统的固有结构，通过求解多个规模较小的POMDP生成用于优先处理与不同人类队友交互的启发式规则，从而缩减状态空间并提升可扩展性。在模拟套件组装任务上的实验结果表明，当机器人策略同时兼顾专业性与影响力时，团队绩效得到显著改善。本研究标志着自适应机器人辅助领域取得重要进展，为将其整合至投资能力有限的中小型低成本产业铺平了道路。