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arXiv · 2311.06391

BUSSARD -- Better Understanding Social Situations for Autonomous Robot Decision-Making

Stefan Schiffer·Astrid Rosenthal-von der Pütten·Bastian Leibe
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Abstract

We report on our effort to create a corpus dataset of different social context situations in an office setting for further disciplinary and interdisciplinary research in computer vision, psychology, and human-robot-interaction. For social robots to be able to behave appropriately, they need to be aware of the social context they act in. Consider, for example, a robot with the task to deliver a personal message to a person. If the person is arguing with an office mate at the time of message delivery, it might be more appropriate to delay playing the message as to respect the recipient's privacy and not to interfere with the current situation. This can only be done if the situation is classified correctly and in a second step if an appropriate behavior is chosen that fits the social situation. Our work aims to enable robots accomplishing the task of classifying social situations by creating a dataset composed of semantically annotated video scenes of office situations from television soap operas. The dataset can then serve as a basis for conducting research in both computer vision and human-robot interaction.

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Stefan Schiffer, Astrid Rosenthal-von der Pütten, Bastian Leibe. 2023-11-10. BUSSARD -- Better Understanding Social Situations for Autonomous Robot Decision-Making. https://arxiv.org/abs/2311.06391

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