Logic-probabilistic model for event recognition in a robotic search and rescue scenario

José A. Gurzoni, Fabio G. Cozman, Murilo F. Martins, Paulo E. Santos

Resultado de la investigación: Contribución a una conferenciaArtículo de conferencia

Resumen

© 2014 IEEE. This paper presents initial results towards the development of a logic-based probabilistic event recognition system capable of learning and inferring high-level joint actions from simultaneous task execution demonstrations on a search and rescue scenario. We adopt a probabilistic extension of the Event Calculus defined over Markov Logic Networks (MLN-EC). This formalism was applied to learn and infer the actions of human operators teleoperating robots in a real-world robotic search and rescue task. Experimental results in both simulation and real robots show that the probabilistic event logic can recognise the actions taken by the human teleoperators in real world domains containing two collaborating robots, even with uncertain and noisy data.
Idioma originalInglés estadounidense
Páginas1726-1731
Número de páginas6
DOI
EstadoPublicada - 1 ene 2014
Publicado de forma externa
EventoConference Proceedings - IEEE International Conference on Systems, Man and Cybernetics -
Duración: 1 ene 2014 → …

Conferencia

ConferenciaConference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
Período1/01/14 → …

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    Gurzoni, J. A., Cozman, F. G., Martins, M. F., & Santos, P. E. (2014). Logic-probabilistic model for event recognition in a robotic search and rescue scenario. 1726-1731. Papel presentado en Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics, . https://doi.org/10.1109/smc.2014.6974166