让机器人用户更高效表达对物体位置的不确定信息。
Uncertainty Expression for Human-Robot Task Communication
- 设计三种界面让用户表达物体位置不确定性：精确输入、绘图热力图、排序列表。
- 排序界面在准确性和效率上表现最佳，绘图界面最不准确。
- 适合人机协作中需传递模糊场景知识的实用场景。
现有许多人类-机器人任务通信方法假设机器人具备充分的环境领域知识，包括关键物体的位置。然而，当已知物体位置发生变化或尚未被机器人发现时，这一假设不成立。本文的核心洞察是，在多数场景中，机器人终端用户比机器人拥有更多场景洞察，需要表达这些知识。目前缺乏关于如何设计收集终端用户场景洞察方案的研究。为此，我们开发了不确定性表达系统（UES），研究如何最佳地获取用户场景洞察。UES允许用户通过三种方式表达对象不确定性：(1) 精确接口，可细致表达场景知识；(2) 绘画接口，用户创建可能物体位置的热力图；(3) 排名接口，用户通过有序列表表达物体位置。随后，我们开展用户研究，比较这三种方法在传达场景洞察的准确性、用户表达效率、可用性及任务负荷方面的表现。结果表明，排名接口比精确接口更友好且高效，而绘画接口准确性最低。
原文摘要 · Abstract (English)
An underlying assumption of many existing approaches to human-robot task communication is that the robot possesses a sufficient amount of environmental domain knowledge, including the locations of task-critical objects. This assumption is unrealistic if the locations of known objects change or have not yet been discovered by the robot. In this work, our key insight is that in many scenarios, robot end users possess more scene insight than the robot and need ways to express it. Presently, there is a lack of research on how solutions for collecting end-user scene insight should be designed. We thereby created an Uncertainty Expression System (UES) to investigate how best to elicit end-user scene insight. The UES allows end users to convey their knowledge of object uncertainty using either: (1) a precision interface that allows meticulous expression of scene insight; (2) a painting interface by which users create a heat map of possible object locations; and (3) a ranking interface by which end users express object locations via an ordered list. We then conducted a user study to compare the effectiveness of these approaches based on the accuracy of scene insight conveyed to the robot, the efficiency at which end users are able to express this scene insight, and both usability and task load. Results indicate that the rank interface is more user friendly and efficient than the precision interface, and that the paint interface is the least accurate.
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