A Bayesian approach for task recognition and future human activity prediction

Vito Magnanimo, Matteo Saveriano, Silvia Rossi, Dongheui Lee

Publikation: KonferenzbeitragPapierBegutachtung

28 Zitate (Scopus)

Abstract

Task recognition and future human activity prediction are of importance for a safe and profitable human-robot cooperation. In real scenarios, the robot has to extract this information merging the knowledge of the task with contextual information from the sensors, minimizing possible misunderstandings. In this paper, we focus on tasks that can be represented as a sequence of manipulated objects and performed actions. The task is modelled with a Dynamic Bayesian Network (DBN), which takes as input manipulated objects and performed actions. Objects and actions are separately classified starting from RGB-D raw data. The DBN is responsible for estimating the current task, predicting the most probable future pairs of action-object and correcting possible misclassification. The effectiveness of the proposed approach is validated on a case of study, consisting of three typical tasks of a kitchen scenario.

OriginalspracheEnglisch
Seiten726-731
Seitenumfang6
DOIs
PublikationsstatusVeröffentlicht - 15 Okt. 2014
Extern publiziertJa
Veranstaltung23rd IEEE International Symposium on Robot and Human Interactive Communication, IEEE RO-MAN 2014 - Edinburgh, Großbritannien/Vereinigtes Königreich
Dauer: 25 Aug. 201429 Aug. 2014

Konferenz

Konferenz23rd IEEE International Symposium on Robot and Human Interactive Communication, IEEE RO-MAN 2014
Land/GebietGroßbritannien/Vereinigtes Königreich
OrtEdinburgh
Zeitraum25/08/1429/08/14

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