论文标题
通过用户反馈对机器人路径计划的个性化解释
Towards Personalized Explanation of Robot Path Planning via User Feedback
论文作者
论文摘要
先前的研究发现，解释机器人决策和行动有助于提高系统透明度，提高用户理解并实现有效的人类机器人协作。在本文中，我们提出了一个系统，用于通过用户反馈生成机器人路径计划的个性化解释。我们考虑在模仿马尔可夫决策过程（MDP）的环境中导航的机器人，并开发算法，以根据用户对四个元素（即客观，本地性，特殊性和语料库）的偏好（即，基于用户的喜好（即，对最佳MDP）策略的个性化解释。此外，我们设计了该系统，通过回答用户有关生成的解释的进一步问题，以与用户进行交互。用户可以选择更新其偏好以查看不同的解释。该系统能够通过用户交互来检测和解决任何偏好冲突。在线用户研究的结果表明，生成的个性化解释提高了用户满意度，而大多数用户喜欢该系统的提问和冲突检测/解决方案的功能。
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.
