Joint Mind Modeling for Explanation Generation in Complex Human-Robot Collaborative Tasks
Joint Mind Modeling for Explanation Generation in Complex Human-Robot Collaborative Tasks
复制标题
用于复杂人机协作任务中解释生成的联合思维建模
DOI：
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
Song
中科院分区：
文献类型：
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作者：
Xiaofeng Gao;Ran Gong;Yizhou Zhao;Shu Wang;Tianmin Shu;Song
人类合作者可以通过推断彼此的心理状态(例如，目标、信仰和愿望)来有效地与他们的伙伴进行沟通，以完成共同的任务。这种有意识的交流最大限度地减少了合作者心理状态之间的差异，对人类自组织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该表现出类似的教学行为。因此，在本文中，我们提出了一种新颖的可解释人工智能(XAI)框架，用于在人-机器人协作中实现类人类交流，其中机器人建立人类用户的分层思维模型，并基于其对用户心理状态的在线贝叶斯推理来生成对自身心理的解释作为一种交流形式。为了评估我们的框架，我们对一个实时的人-机器人烹饪任务进行了用户研究。实验结果表明，该方法生成的解释显著提高了机器人的协作性能和用户感知能力。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/.