Towards Deep Reasoning on Social Rules for Socially Aware Navigation
| Autor: | Roya Salek Shahrezaie, Mohammadmahdi Mohammadi, David Feil-Seifer, Santosh Balajee Banisetty |
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| Rok vydání: | 2021 |
| Předmět: |
0209 industrial biotechnology
business.industry Computer science media_common.quotation_subject Context (language use) Social navigation 02 engineering and technology Convolutional neural network Human–robot interaction Object detection 020901 industrial engineering & automation Knowledge base Human–computer interaction 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Quality (business) business Classifier (UML) media_common |
| Zdroj: | HRI (Companion) |
| DOI: | 10.1145/3434074.3447225 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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