A Bayesian approach for task recognition and future human activity prediction

Vito Magnanimo, Matteo Saveriano, Silvia Rossi, Dongheui Lee

Research output: Contribution to conferencePaperpeer-review

28 Scopus citations

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.

Original languageEnglish
Pages726-731
Number of pages6
DOIs
StatePublished - 15 Oct 2014
Externally publishedYes
Event23rd IEEE International Symposium on Robot and Human Interactive Communication, IEEE RO-MAN 2014 - Edinburgh, United Kingdom
Duration: 25 Aug 201429 Aug 2014

Conference

Conference23rd IEEE International Symposium on Robot and Human Interactive Communication, IEEE RO-MAN 2014
Country/TerritoryUnited Kingdom
CityEdinburgh
Period25/08/1429/08/14

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