Socially-Aware Navigation: Action Discrimination to Select Appropriate Behavior
Socially-Aware Navigation: Action Discrimination to Select Appropriate Behavior
复制标题
社会意识导航：动作歧视以选择适当的行为
DOI：
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
David Feil
中科院分区：
文献类型：
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作者：
S. Banisetty;Meera Sebastian;David Feil
在本文中，我们研究建模是否有助于区分行为，而这些行为又可用于为移动机器人选择适当的行为。对于人与人之间的互动，重要的社交和交流信息可从两个或更多人之间的人际距离中得出。如果人机交互反映了这种人与人互动的特性，那么人与机器人之间的人际距离可能包含类似的社交和交流信息。机器人的有效行为，包括与人际距离相关的行为，必须适合给定的社会环境。研究机器人与人之间的人际距离对于辅助机器人非常重要。我们使用自主检测的特征，利用高斯混合模型（GMM）来建立这样一种人际模型，并证明这种学习到的模型能够区分不同的人类行为。所提出的方法可用于具有社会感知的规划器中，以对轨迹进行加权并选择在给定社会情境下符合社会规范的行为。
In this paper, we study if modeling can help discriminate actions which in turn can be used to select an appropriate behavior for a mobile robot. For human-human interaction, significant social and communicative information can be derived from interpersonal distance between two or more people. If Human-Robot Interaction reflects this human-human interaction property, then interpersonal distance between a human and a robot may contain similar social and communicative information. An effective robot’s actions, including actions associated with interpersonal distance, must be suitable for a given social circumstance. Studying interpersonal distance between a robot and a human is very important for assistive robots. We use autonomously detected features to develop such an interpersonal model using Gaussian Mixture Model (GMM) and demonstrate that such a learned model can discriminate different human actions. The proposed approach can be used in a socially-aware planner to weight trajectories and select actions that are socially appropriate for a given social situation.