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Learning the semantics of object-action relations by observation

AutorAksoy, Eren Erdal; Abramov, Alexey; Dörr, Johannes; Ning, Kejun; Dellen, Babette ; Wörgötter, Florentin
Palabras claveAction recognition
Object categorization
Semantic scene graphs Unsupervised learning
Object–action complexes (OACs)
Fecha de publicación2011
EditorSage Publications
CitaciónInternational Journal of Robotics Research 30(10): 1229-1249 (2011)
ResumenRecognizing 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. © SAGE Publications 2011.
Versión del editorhttp://dx.doi.org/10.1177/0278364911410459
Identificadoresdoi: 10.1177/0278364911410459
issn: 0278-3649
e-issn: 1741-3176
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