As robots are deployed in human spaces, it's important that they are able to coordinate their actions with the people around them. Part of such coordination involves ensuring that people have a good understanding of how a robot will act in the environment. This can be achieved through explanations of the robot's policy. Much prior work in explainable AI and RL focuses on generating explanations for single-agent policies, but little has been explored in generating explanations for collaborative policies. In this work, we investigate how to generate multi-agent strategy explanations for human-robot collaboration. We formulate the problem using a generic multi-agent planner, show how to generate visual explanations through strategy-conditioned landmark states and generate textual explanations by giving the landmarks to an LLM. Through a user study, we find that when presented with explanations from our proposed framework, users are able to better explore the full space of strategies and collaborate more efficiently with new robot partners.

翻译：随着机器人在人类空间中部署，确保其能够与周围人群协调行动至关重要。这种协调的一部分涉及确保人们充分理解机器人在环境中的行为方式，这可以通过对机器人策略的解释来实现。以往大量可解释人工智能与强化学习领域的研究集中于为单智能体策略生成解释，但针对协作策略的解释生成方法尚待深入探索。本研究探讨了如何为人机协同生成多智能体策略解释。我们通过通用多智能体规划器构建问题模型，展示了如何通过策略条件化地标状态生成视觉解释，并通过将地标输入大语言模型生成文本解释的方法。用户研究表明，当参与者获得基于我们框架生成的解释时，能够更全面地探索策略空间，并与新机器人伙伴实现更高效的协作。