HRI数据汇集过程的形式化
Towards Formalizing HRI Data Collection Processes
摘要：
在人机交互（HRI）领域，许多研究人员都专注于人类受试者研究的精心设计。然而，社区的其他部分，例如，技术进步团体也需要进行人类受试者研究，以收集数据来训练他们的模型，这种方法需要用户研究，但没有严格的实验设计。这种数据汇集的设计是一个值得更多关注的探索领域。在这项工作中，我们贡献了一个明确定义的过程，收集数据的三个步骤为机器学习建模的目的，接地在最近的文献，并详细说明了使用这个过程，以方便参考表达的语料库的收集。具体来说，我们讨论了我们的数据汇集的目标，以及我们如何工作，以鼓励覆盖面广，丰富的参与者的反应，通过我们的设计任务环境，任务本身，和研究程序。我们希望这项工作将导致更多的数据汇集形式主义的努力，在人权研究所社区和富有成效的讨论在讲习班期间。
英文摘要：
Within the human-robot interaction (HRI) community, many researchers have focused on the careful design of human-subjects studies. However, other parts of the community, e.g., the technical advances community, also need to do human-subjects studies to collect data to train their models, in ways that require user studies but without a strict experimental design. The design of such data collection is an underexplored area worthy of more attention. In this work, we contribute a clearly defined process to collect data with three steps for machine learning modeling purposes, grounded in recent literature, and detail an use of this process to facilitate the collection of a corpus of referring expressions. Specifically, we discuss our data collection goal and how we worked to encourage well-covered and abundant participant responses, through our design of the task environment, the task itself, and the study procedure. We hope this work would lead to more data collection formalism efforts in the HRI community and a fruitful discussion during the workshop.
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