In this work, we focused on constructing and evaluating levels of explanation(LOE) that address two basic aspect of HRI: 1. What information should be communicated to the user by the robot? 2. When should the robot communicate this information? For constructing the LOE, we defined two terms, verbosity and explanation patterns, each with two levels (verbosity -- high and low, explanation patterns -- dynamic and static). Based on these parameters, three different LOE (high, medium, and low) were constructed and evaluated in a user study with a telepresence robot. The user study was conducted for a simulated telerobotic healthcare task with two different conditions related to time sensitivity, as evaluated by two different user groups -- one that performed the task within a time limit and the other with no time limit. We found that the high LOE was preferred in terms of adequacy of explanation, number of collisions, number of incorrect movements, and number of clarifications when users performed the experiment in the without time limit condition. We also found that both high and medium LOE did not have significant differences in completion time, the fluency of HRI, and trust in the robot. When users performed the experiment in the with time limit condition, high and medium LOE had better task performances and were preferred to the low LOE in terms of completion time, fluency, adequacy of explanation, trust, number of collisions, number of incorrect movements and number of clarifications. Future directions for advancing LOE are discussed.

翻译：本工作聚焦于构建和评估解释层级(LOE)，以解决人机交互(HRI)中的两个基本问题：1.机器人应向用户传递何种信息？2.机器人应在何时传递这些信息？在LOE构建中，我们定义了两个参数——详细程度与解释模式，每个参数包含两个层级（详细程度——高与低；解释模式——动态与静态）。基于这些参数，我们构建了三种不同的LOE（高、中、低），并通过远程呈现机器人进行了用户研究评估。该用户研究针对模拟远程医疗任务展开，设置了两种与时间敏感性相关的实验条件，并由两个不同的用户组进行评估——一组在时间限制下执行任务，另一组无时间限制。研究发现，在无时间限制条件下，高LOE在解释充分性、碰撞次数、错误移动次数及澄清请求次数方面更受青睐。同时，高LOE与中LOE在任务完成时间、HRI流畅度及对机器人的信任度方面未呈现显著差异。在时间限制条件下，高LOE与中LOE表现出更优的任务性能，且在完成时间、流畅度、解释充分性、信任度、碰撞次数、错误移动次数及澄清请求次数方面均优于低LOE。本文最后探讨了推进LOE研究的未来方向。