The growing interest in human-robot collaboration (HRC), where humans and robots cooperate towards shared goals, has seen significant advancements over the past decade. While previous research has addressed various challenges, several key issues remain unresolved. Many domains within HRC involve activities that do not necessarily require human presence throughout the entire task. Existing literature typically models HRC as a closed system, where all agents are present for the entire duration of the task. In contrast, an open model offers flexibility by allowing an agent to enter and exit the collaboration as needed, enabling them to concurrently manage other tasks. In this paper, we introduce a novel multiagent framework called oDec-MDP, designed specifically to model open HRC scenarios where agents can join or leave tasks flexibly during execution. We generalize a recent multiagent inverse reinforcement learning method - Dec-AIRL to learn from open systems modeled using the oDec-MDP. Our method is validated through experiments conducted in both a simplified toy firefighting domain and a realistic dyadic human-robot collaborative assembly. Results show that our framework and learning method improves upon its closed system counterpart.

翻译：过去十年间，人机协作（HRC）——即人类与机器人为实现共同目标而合作——领域的研究兴趣日益增长，并取得了显著进展。尽管已有研究解决了诸多挑战，若干关键问题仍未得到充分解决。HRC中的许多应用场景涉及的活动并不要求人类在整个任务过程中全程在场。现有文献通常将HRC建模为封闭系统，即所有智能体在整个任务期间均持续参与。相比之下，开放模型通过允许智能体根据需要加入或退出协作，使其能够同时处理其他任务，从而提供了更高的灵活性。本文提出了一种称为oDec-MDP的新型多智能体框架，专门用于建模开放HRC场景，其中智能体可在任务执行过程中灵活加入或离开。我们推广了近期的一种多智能体逆强化学习方法——Dec-AIRL，使其能够从基于oDec-MDP建模的开放系统中进行学习。通过在简化的玩具消防模拟场景和真实的人机双人协作装配任务中进行实验验证，结果表明我们的框架与学习方法相较于封闭系统模型具有更优性能。