论文标题
在复杂的人类机器人协作任务中的解释产生的联合思维建模
Joint Mind Modeling for Explanation Generation in Complex Human-Robot Collaborative Tasks
论文作者
论文摘要
人类合作者可以通过推断彼此的心理状态（例如目标，信念和欲望）来有效地与他们的合作伙伴沟通，以完成一项共同的任务。这种思想感知的沟通最大程度地减少了合作者的心理状态之间的差异，并且对人类临时团队的成功至关重要。我们认为，与人类用户合作的机器人应表现出类似的教学行为。因此，在本文中，我们提出了一个可解释的AI（XAI）框架，用于实现人类机器人合作中的类似人类的沟通，在该框架中，机器人在其中建立了人类用户的分层思维模型，并根据其在线贝叶斯对用户的心理状态的界定，将其自身思想的解释作为一种交流形式。为了评估我们的框架，我们对实时人类机器人烹饪任务进行了用户研究。实验结果表明，对我们方法的生成解释显着提高了机器人的协作绩效和用户的看法。代码和视频演示可在我们的项目网站上找到：https：//xfgao.github.io/xcookingweb/。
Human collaborators can effectively communicate with their partners to finish a common task by inferring each other's mental states (e.g., goals, beliefs, and desires). Such mind-aware communication minimizes the discrepancy among collaborators' mental states, and is crucial to the success in human ad-hoc teaming. We believe that robots collaborating with human users should demonstrate similar pedagogic behavior. Thus, in this paper, we propose a novel explainable AI (XAI) framework for achieving human-like communication in human-robot collaborations, where the robot builds a hierarchical mind model of the human user and generates explanations of its own mind as a form of communications based on its online Bayesian inference of the user's mental state. To evaluate our framework, we conduct a user study on a real-time human-robot cooking task. Experimental results show that the generated explanations of our approach significantly improves the collaboration performance and user perception of the robot. Code and video demos are available on our project website: https://xfgao.github.io/xCookingWeb/.
