On Using Social Signals to Enable Flexible Error-Aware HRI
On Using Social Signals to Enable Flexible Error-Aware HRI
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DOI：
10.1145/3568162.3576990
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发表时间：
2023-03
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影响因子：
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通讯作者：
Maia Stiber;R. Taylor;Chien-Ming Huang
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作者：
Maia Stiber;R. Taylor;Chien-Ming Huang
现有的错误管理技术通常不具备跨任务和场景适当地解决机器人错误的多功能性。他们的基本框架包括明确的，手动的错误管理和隐式的特定领域的信息驱动的错误管理，定制他们的响应特定的交互环境。我们提出了一个框架，通过添加隐含的社会信号作为另一个信息渠道，以创造更多的灵活性，在应用程序中接近错误感知系统。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），重点是了解自然面部动作单元（Au）在基于物理的人机交互过程中对机器人错误的反应-在任务、错误、人员和场景中变化。对数据集的分析表明，通过错误检测的透镜，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，我们提供了一个示例实时交互式机器人错误管理系统使用的错误感知框架。
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.