Shipeng Liu, Cristina G. Wilson, Bhaskar Krishnamachari, Feifei Qian
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引用次数: 0
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.
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.