Prior studies have found that explaining robot decisions and actions helps to increase system transparency, improve user understanding, and enable effective human-robot collaboration. In this paper, we present a system for generating personalized explanations of robot path planning via user feedback. We consider a robot navigating in an environment modeled as a Markov decision process (MDP), and develop an algorithm to automatically generate a personalized explanation of an optimal MDP policy, based on the user preference regarding four elements (i.e., objective, locality, specificity, and corpus). In addition, we design the system to interact with users via answering users' further questions about the generated explanations. Users have the option to update their preferences to view different explanations. The system is capable of detecting and resolving any preference conflict via user interaction. The results of an online user study show that the generated personalized explanations improve user satisfaction, while the majority of users liked the system's capabilities of question-answering and conflict detection/resolution.
翻译:先前的研究发现,解释机器人的决定和行动有助于提高系统透明度,提高用户理解度,并促成有效的人类机器人合作。在本文中,我们提出了一个通过用户反馈对机器人路径规划进行个性化解释的系统。我们认为,在以Markov决定程序(MDP)为模型的环境中,机器人在一种环境中航行,并开发一种算法,根据用户对四个要素(即目标、地点、特性和内容)的偏好,自动对最佳MDP政策作出个性化解释。此外,我们设计这个系统,通过回答用户关于所产生解释的进一步问题与用户互动。用户可以选择更新其选择,查看不同的解释。这个系统能够通过用户互动探测和解决任何偏好冲突。在线用户研究结果显示,产生的个性化解释提高了用户的满意度,而大多数用户则喜欢这个系统的问答和冲突探测/解决能力。