On Using Social Signals to Enable Flexible Error-Aware HRI

Maia Stiber, R. Taylor, Chien-Ming Huang
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引用次数: 5

Abstract

Prior error management techniques often do not possess the versatility to appropriately address robot errors across tasks and scenarios. Their fundamental framework involves explicit, manual error management and implicit domain-specific information driven error management, tailoring their response for specific interaction contexts. We present a framework for approaching error-aware systems by adding implicit social signals as another information channel to create more flexibility in application. To support this notion, we introduce a novel dataset (composed of three data collections) with a focus on understanding natural facial action unit (AU) responses to robot errors during physical-based human-robot interactions---varying across task, error, people, and scenario. Analysis of the dataset reveals that, through the lens of error detection, using AUs as input into error management affords flexibility to the system and has the potential to improve error detection response rate. In addition, we provide an example real-time interactive robot error management system using the error-aware framework.
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利用社会信号实现灵活的错误感知HRI
先前的错误管理技术通常不具备跨任务和场景适当处理机器人错误的通用性。它们的基本框架包括显式的手动错误管理和隐式的特定于领域的信息驱动的错误管理,为特定的交互上下文定制它们的响应。我们提出了一个框架,通过增加隐式社会信号作为另一个信息通道来接近错误感知系统,以创造更大的应用灵活性。为了支持这一概念,我们引入了一个新的数据集(由三个数据集组成),重点是理解基于物理的人机交互过程中对机器人错误的自然面部动作单元(AU)响应——在任务、错误、人员和场景之间变化。对数据集的分析表明,从错误检测的角度来看,使用au作为错误管理的输入为系统提供了灵活性,并有可能提高错误检测响应率。此外,我们还提供了一个使用错误感知框架的实时交互式机器人错误管理系统示例。
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来源期刊
ACM Transactions on Human-Robot Interaction
ACM Transactions on Human-Robot Interaction Computer Science-Artificial Intelligence
CiteScore
7.70
自引率
5.90%
发文量
65
期刊介绍: ACM Transactions on Human-Robot Interaction (THRI) is a prestigious Gold Open Access journal that aspires to lead the field of human-robot interaction as a top-tier, peer-reviewed, interdisciplinary publication. The journal prioritizes articles that significantly contribute to the current state of the art, enhance overall knowledge, have a broad appeal, and are accessible to a diverse audience. Submissions are expected to meet a high scholarly standard, and authors are encouraged to ensure their research is well-presented, advancing the understanding of human-robot interaction, adding cutting-edge or general insights to the field, or challenging current perspectives in this research domain. THRI warmly invites well-crafted paper submissions from a variety of disciplines, encompassing robotics, computer science, engineering, design, and the behavioral and social sciences. The scholarly articles published in THRI may cover a range of topics such as the nature of human interactions with robots and robotic technologies, methods to enhance or enable novel forms of interaction, and the societal or organizational impacts of these interactions. The editorial team is also keen on receiving proposals for special issues that focus on specific technical challenges or that apply human-robot interaction research to further areas like social computing, consumer behavior, health, and education.
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