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Títol: Learning the semantics of object-action relations by observation
Autor: Aksoy, Eren Erdal; Abramov, Alexey; Dörr, Johannes; Ning, Kejun; Dellen, Babette Veure Producció científica UPC; Wörgötter, Florentin
Data: 28-oct-2011
Tipus de document: Article
Resum: Recognizing manipulations performed by a human and the transfer and execution of this by a robot is a difficult problem. We address this in the current study by introducing a novel representation of the relations between objects at decisive time points during a manipulation. Thereby, we encode the essential changes in a visual scenery in a condensed way such that a robot can recognize and learn a manipulation without prior object knowledge. To achieve this we continuously track image segments in the video and construct a dynamic graph sequence. Topological transitions of those graphs occur whenever a spatial relation between some segments has changed in a discontinuous way and these moments are stored in a transition matrix called the semantic event chain (SEC). We demonstrate that these time points are highly descriptive for distinguishing between different manipulations. Employing simple sub-string search algorithms, SECs can be compared and type-similar manipulations can be recognized with high confidence. As the approach is generic, statistical learning can be used to find the archetypal SEC of a given manipulation class. The performance of the algorithm is demonstrated on a set of real videos showing hands manipulating various objects and performing different actions. In experiments with a robotic arm, we show that the SEC can be learned by observing human manipulations, transferred to a new scenario, and then reproduced by the machine.
ISSN: 0278-3649
URI: http://hdl.handle.net/2117/14016
DOI: 10.1177/0278364911410459
Versió de l'editor: http://dx.doi.org/10.1177/0278364911410459
Apareix a les col·leccions:Altres. Enviament des de DRAC
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