Please use this identifier to cite or link to this item:https://hdl.handle.net/20.500.12259/126809
Type of publication: research article
Type of publication (PDB): Straipsnis Clarivate Analytics Web of Science / Article in Clarivate Analytics Web of Science (S1)
Field of Science: Informatika / Informatics (N009)
Author(s): Wörgötter, Florentin;Ziaeetabar, F;Pfeifer, S;Kaya, O;Kulvičius, Tomas;Tamošiūnaitė, Minija
Title: Humans predict action using grammar-like structures
Is part of: Scientific reports [electronic resource]. London : Nature Publishing Group, 2020, vol. 10
Extent: p. 1-11
Date: 2020
Note: Article no. 3999
Keywords: Veiksmų prognozavimas;Manipulicijų veiksmai;Žmogaus veiksmai;Bendradarbiavimas;Robotas;Action prediction;Manipulation action;Human actions;Cooperation;Robot
Abstract: Efficient action prediction is of central importance for the fluent workflow between humans and equally so for human-robot interaction. To achieve prediction, actions can be algorithmically encoded by a series of events, where every event corresponds to a change in a (static or dynamic) relation between some of the objects in the scene. These structures are similar to a context-free grammar and, importantly, within this framework the actual objects are irrelevant for prediction, only their relational changes matter. Manipulation actions and others can be uniquely encoded this way. Using a virtual reality setup and testing several different manipulation actions, here we show that humans predict actions in an event-based manner following the sequence of relational changes. Testing this with chained actions, we measure the percentage predictive temporal gain for humans and compare it to action-chains performed by robots showing that the gain is approximately equal. Event-based and, thus, object independent action recognition and prediction may be important for cognitively deducing properties of unknown objects seen in action, helping to address bootstrapping of object knowledge especially in infants
Internet: https://www.vdu.lt/cris/bitstream/20.500.12259/126809/2/ISSN2045-2322_2020_V_10.PG_1-11.pdf
https://hdl.handle.net/20.500.12259/126809
https://doi.org/10.1038/s41598-020-60923-5
Affiliation(s): Taikomosios informatikos katedra
Vytauto Didžiojo universitetas
Appears in Collections:1. Straipsniai / Articles
Universiteto mokslo publikacijos / University Research Publications

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