Understanding Human Dynamic Sampling Objectives to Enable Robot-assisted Scientific Decision Making

IF 4.2 Q2 ROBOTICS
Shipeng Liu, Cristina G. Wilson, Bhaskar Krishnamachari, Feifei Qian
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Abstract

Truly collaborative scientific field data collection between human scientists and autonomous robot systems requires a shared understanding of the search objectives and tradeoffs faced when making decisions. Therefore, critical to developing intelligent robots to aid human experts, is an understanding of how scientists make such decisions and how they adapt their data collection strategies when presented with new information in situ . In this study we examined the dynamic data collection decisions of 108 expert geoscience researchers using a simulated field scenario. Human data collection behaviors suggested two distinct objectives: an information-based objective to maximize information coverage, and a discrepancy-based objective to maximize hypothesis verification. We developed a highly-simplified quantitative decision model that allows the robot to predict potential human data collection locations based on the two observed human data collection objectives. Predictions from the simple model revealed a transition from information-based to discrepancy-based objective as the level of information increased. The findings will allow robotic teammates to connect experts’ dynamic science objectives with the adaptation of their sampling behaviors, and in the long term, enable the development of more cognitively-compatible robotic field assistants.
理解人类动态采样目标,使机器人辅助科学决策
人类科学家和自主机器人系统之间真正的协作科学领域数据收集需要对搜索目标和决策时面临的权衡有共同的理解。因此,开发智能机器人来帮助人类专家的关键是了解科学家如何做出这样的决定,以及他们如何在现场呈现新信息时调整他们的数据收集策略。在这项研究中,我们使用模拟的现场场景检查了108位地球科学专家的动态数据收集决策。人类数据收集行为表明了两个不同的目标:基于信息的目标是最大化信息覆盖,基于差异的目标是最大化假设验证。我们开发了一个高度简化的定量决策模型,允许机器人根据两个观察到的人类数据收集目标预测潜在的人类数据收集位置。简单模型的预测显示,随着信息水平的增加,目标从基于信息到基于差异的转变。这些发现将使机器人队友能够将专家的动态科学目标与他们的采样行为的适应联系起来,从长远来看,能够开发出更多认知兼容的机器人现场助理。
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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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