Towards Deep Reasoning on Social Rules for Socially Aware Navigation

Towards Deep Reasoning on Social Rules for Socially Aware Navigation
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对社会规则进行深度推理以实现社会意识导航

DOI:
10.1145/3434074.3447225
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发表时间:
2021
期刊:
HRI '21 Companion: Companion of the 2021 ACM/IEEE International Conference on Human-Robot Interaction
影响因子:
--
通讯作者:
Feil-Seifer, David
Feil-Seifer, David
中科院分区:
--
文献类型:
--
作者:
Salek Shahrezaie, Roya;Banisetty, Santosh Balajee;Mohammadi, Mohammadmahdi;Feil-Seifer, David

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这项工作提出了构思和初步的结果,使用上下文信息和信息的对象存在于场景中查询适用的社交导航规则的感知上下文。社会感知导航(SAN)的先前工作显示了其在人机交互中的重要性,因为它提高了交互伙伴的交互质量,安全性和舒适性。在这项工作中,我们感兴趣的是在SAN中的社会规则的自动检测,我们提出了我们的方法的三个主要组成部分,即:基于卷积神经网络的上下文分类器,可以自主感知上下文信息使用相机输入,基于YOLO的对象检测,以定位对象的场景,以及社会规则与概念的关系的知识库,以使用场景中的上下文对象和检测到的对象来查询它们。我们的初步结果表明,我们的方法可以观察到一个正在进行的互动,给定的图像输入,并使用该信息来查询所需的社会导航规则,在特定的上下文中。
This work presents ideation and preliminary results of using contextual information and information of the objects present in the scene to query applicable social navigation rules for the sensed context. Prior work in socially-Aware Navigation (SAN) shows its importance in human-robot interaction as it improves the interaction quality, safety and comfort of the interacting partner. In this work, we are interested in automatic detection of social rules in SAN and we present three major components of our method, namely: a Convolutional Neural Network-based context classifier that can autonomously perceive contextual information using camera input; a YOLO-based object detection to localize objects with a scene; and a knowledge base of social rules relationships with the concepts to query them using both contextual and detected objects in the scene. Our preliminary results suggest that our approach can observe an on-going interaction, given an image input, and use that information to query the social navigation rules required in that particular context.
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