Robot Deictics: How Gesture and Context Shape Referential Communication
Robot Deictics: How Gesture and Context Shape Referential Communication
复制标题
机器人指示学：手势和上下文如何塑造参考通信
DOI：
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发表时间：
2014
期刊：
影响因子：
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通讯作者：
Bilge Mutlu
中科院分区：
文献类型：
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作者：
Allison Sauppé;Bilge Mutlu
随着机器人在日益多样化的环境中与人类协作，它们将需要有效地指代共同关注的对象，并根据各种物理、环境和任务条件调整其指代方式。人类使用广泛的指示性手势——将注意力引向并置的物体、人或空间的手势，包括指向、触摸和展示，以帮助听众理解其指代内容。这些手势在不同条件下提供不同程度的支持，使得某些手势或多或少适合不同的情境。虽然这些手势为设计机器人的交流行为提供了广阔空间，但更好地理解不同指示性手势在不同条件下如何影响交流对于实现有效的人机交互至关重要。在本文中，我们试图通过在类人机器人上实现六种指示性手势，并在六种不同的环境中评估它们的交流效果来建立这样的理解，这些环境代表了机器人预期使用指示性交流的物理、环境和任务条件。我们的结果表明，与物体有物理接触的手势在整体交流准确性方面最高，并且特定环境受益于特定类型手势的使用。我们的结果凸显了指示性手势丰富的设计空间，并为机器人如何根据特定的物理、环境和任务条件调整其手势提供了信息。
分类和主题描述符
H.1.2 [模型与原理]：用户/机器系统——人为因素，软件心理学；
H.5.2 [信息界面与呈现]：用户界面——评估/方法，以用户为中心的设计。
通用术语
设计，人为因素
As robots collaborate with humans in increasingly diverse environments, they will need to effectively refer to objects of joint interest and adapt their references to various physical, environmental, and task conditions. Humans use a broad range of deictic gestures—gestures that direct attention to collocated objects, persons, or spaces—that include pointing, touching, and exhibiting to help their listeners understand their references. These gestures offer varying levels of support under different conditions, making some gestures more or less suitable for different settings. While these gestures offer a rich space for designing communicative behaviors for robots, a better understanding of how different deictic gestures affect communication under different conditions is critical for achieving effective human-robot interaction. In this paper, we seek to build such an understanding by implementing six deictic gestures on a humanlike robot and evaluating their communicative effectiveness in six diverse settings that represent physical, environmental, and task conditions under which robots are expected to employ deictic communication. Our results show that gestures which come into physical contact with the object offer the highest overall communicative accuracy and that specific settings benefit from the use of particular types of gestures. Our results highlight the rich design space for deictic gestures and inform how robots might adapt their gestures to the specific physical, environmental, and task conditions. Categories and Subject Descriptors H.1.2 [Models and Principles]: User/Machine Systems— human factors, software psychology; H.5.2 [Information Interfaces and Presentation]: User Interfaces— evaluation/methodology, usercentered design. General Terms Design, Human Factors