Nonverbal Behavior Modeling for Socially Assistive Robots
Nonverbal Behavior Modeling for Socially Assistive Robots
复制标题
社交辅助机器人的非语言行为建模
DOI：
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发表时间：
2014
期刊：
影响因子：
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通讯作者：
B. Scassellati
中科院分区：
文献类型：
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作者：
H. Admoni;B. Scassellati
社会辅助机器人（SAR）领域旨在构建通过社会互动帮助人们的机器人。人类社会互动涉及复杂的行为系统，对这些系统进行建模是SAR的目标之一。非语言行为，如眼睛凝视和手势，特别适合通过机器学习建模，因为系统的影响-非语言行为本身-是天生可观察的。揭示内斯这些行为的基本模型将使社交辅助机器人成为更好的互动伙伴。我们的研究调查了人们如何在辅导应用程序中使用非语言行为。我们使用来自人与人互动的数据，使用监督机器学习来构建非语言行为模型。该模型既能预测所观察到的行为的语境，又能产生适当的非言语行为。
The field of socially assistive robotics (SAR) aims to build robots that help people through social interaction. Human social interaction involves complex systems of behavior, and modeling these systems is one goal of SAR. Nonverbal behaviors, such as eye gaze and gesture, are particularly amenable to modeling through machine learning because the effects of the system—the nonverbal behaviors themselves— are inherently observable. Uncovering the underlying model that defines those behaviors would allow socially assistive robots to become better interaction partners. Our research investigates how people use nonverbal behaviors in tutoring applications. We use data from human-human interactions to build a model of nonverbal behaviors using supervised machine learning. This model can both predict the context of observed behaviors and generate appropriate nonverbal behaviors.