促进机器人与儿童互动的基于距离的计算模型

David Feil-Seifer, M. Matarić
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引用次数: 45

摘要

感知和解释用户的活动和社会行为,监测社会环境的动态,选择和产生适当的机器人动作是在交互场景中使用机器人作为社会工具所涉及的核心挑战。在人机交互中,语音和手势是常用的交互方式。在人与人之间的互动中,人与人之间的人际距离可以包含重要的社会和交际信息。因此,如果人机交互反映了这种人机交互特性,那么人机距离也传递了社会信息。如果一个机器人要成为一个有效的社会代理人,它的行为,包括那些与人际距离有关的行为,必须适合给定的社会情境。在游戏和非结构化的互动中,比如涉及儿童的互动,这就成为了一个更大的挑战。本文展示了使用基于距离的模型来识别和表达空间社会行为。该模型旨在对儿童在与机器人进行有趣互动时的厌恶社会行为进行分类,并使用基于距离的特征来自主识别互动/游戏,回避,墙壁拥抱和父母接近行为,准确率为94%。同样的方法被用于模拟一个人跟随机器人的空间方面,并将该模型作为改进的导航计划的一部分,使机器人能够表现出社会意识的目标导向的导航行为。基于模型的规划器导致机器人导航行为更有效地允许伙伴跟随机器人。这种效果是用导航性能的定量测量和机器人行为的观察者评级来证明的。空间模型的这两种用途在完整的机器人系统上实现,并在自闭症谱系障碍儿童和神经正常成人的评估研究中得到验证。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Distance-based computational models for facilitating robot interaction with children
Sensing and interpreting the user's activities and social behavior, monitoring the dynamics of the social context, and selecting and producing appropriate robot action are the core challenges involved in using robots as social tools in interaction scenarios. In social human-robot interaction, speech and gesture are the commonly considered interaction modalities. In human-human interactions, interpersonal distance between people can contain significant social and communicative information. Thus, if human-robot interaction reflects this human-human interaction property, then human-robot distances also convey social information. If a robot is to be an effective social agent, its actions, including those relating to interpersonal distance, must be appropriate for the given social situation. This becomes a greater challenge in playful and unstructured interactions, such as those involving children. This paper demonstrates the use of a distance-based model for the recognition and expression of spatial social behavior. The model was designed to classify averse social behavior of a child engaged in playful interaction with a robot and uses distance-based features to autonomously identify interaction/play, avoidance, wall-hugging, and parent-proximity behavior with 94% accuracy. The same methodology was used to model the spatial aspects of a person following a robot and use the model as part of a modified navigation planner to enable the robot to exhibit socially-aware goal-oriented navigation behavior. The model-based planner resulted in robot navigation behavior that was more effective at allowing a partner to follow the robot. This effect was demonstrated using quantitative measures of navigation performance and observer rating of the robot's behavior. These two uses of spatial models were implemented on complete robot systems and validated in evaluation studies with children with autism spectrum disorders and with neurotypical adults.
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