Abstract
Robots are becoming ever more present in households, interacting more with humans. They are able to perform tasks in an accurate manner, e.g. manipulating objects. However, this manipulation often does not follow the human way to arrange objects. Therefore, robots require semantic knowledge about the environment for executing tasks and satisfying humans’ expectations. In this paper, we will introduce a breakfast table setting scenario where a robot acquires information from human demonstrations to arrange objects in a meaningful way. We will show how robots can obtain the necessary amount of knowledge to autonomously perform daily tasks.
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Acknowledgements
This work is partially funded by Deutsche Forschungsgemeinschaft (DFG) through the Collaborative Research Center 1320, EASE. Lisset Salinas Pinacho and Alexander Wich acknowledge support from the German Academic Exchange Service (DAAD) and the Don Carlos Antonio López (BECAL) PhD scholarships, respectively. We also thank Matthias Schneider for his help in the revision of this work.
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Salinas Pinacho, L., Wich, A., Yazdani, F., Beetz, M. (2018). Acquiring Knowledge of Object Arrangements from Human Examples for Household Robots. In: Trollmann, F., Turhan, AY. (eds) KI 2018: Advances in Artificial Intelligence. KI 2018. Lecture Notes in Computer Science(), vol 11117. Springer, Cham. https://doi.org/10.1007/978-3-030-00111-7_12
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DOI: https://doi.org/10.1007/978-3-030-00111-7_12
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