Abstract
Human demonstrations of everyday activities are an important resource to learn the particularities of the corresponding control strategies that are needed to perform such activities with ease and competence. However, such demonstrations need to be annotated such that time segments get associated to the appropriate actions. Previous research in psychology has shown that humans find contact and force events to be particularly significant when adapting control situations during a task. Based on the psychologically motivated Flanagan model, we present a method to recognize activities from force dynamic events and states. For this, we incorporated the Flanagan model in an ontology, together with Allen’s interval algebra to model temporal ordering constraints. We use the ontology to generate the grammar of an activity parser. Due to this parser creation method, the system can also be used as a verification tool for the ontology.
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Acknowledgements
The research reported in this paper has been (partially) supported by the German Research Foundation DFG, as part of Collaborative Research Center (Sonderforschungsbereich) 1320 ‘'EASE - Everyday Activity Science and Engineering'‘, University of Bremen (http://www.ease-crc.org/). The research was conducted in subprojects H02, P01 and R01 as well as the FET-Open Project #951846 “MUHAI -- Meaning and Understanding for Human-centric AI” funded by the EU Program Horizon 2020.
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Beßler, D., Porzel, R., Pomarlan, M., Beetz, M. (2023). Foundational Models for Manipulation Activity Parsing. In: Jung, T., tom Dieck, M.C., Correia Loureiro, S.M. (eds) Extended Reality and Metaverse. XR 2022. Springer Proceedings in Business and Economics. Springer, Cham. https://doi.org/10.1007/978-3-031-25390-4_10
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