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Learning Multirobot Joint Action Plans from Simultaneous Task Execution Demonstrations ABSTRACT
| Citations: | 11 - 5 self |
BibTeX
@MISC{Martins_learningmultirobot,
author = {Murilo Fernandes Martins},
title = {Learning Multirobot Joint Action Plans from Simultaneous Task Execution Demonstrations ABSTRACT},
year = {}
}
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Abstract
The central problem of designing intelligent robot systems which learn by demonstrations of desired behaviour has been largely studied within the field of robotics. Numerous architectures for action recognition and prediction of intent of a single teacher have been proposed. However, little work has been done addressing how a group of robots can learn by simultaneous demonstrations of multiple teachers. This paper contributes a novel approach for learning multirobot joint action plans from unlabelled data. The robots firstly learn the demonstrated sequence of individual actions using the HAMMER architecture. Subsequently, the group behaviour is segmented over time and space by applying a spatio-temporal clustering algorithm. The experimental results, in which humans teleoperated real robots during a search and rescue task deployment, successfully demonstrated the efficacy of combining action recognition at individual level with group behaviour segmentation, spotting the exact moment when robots must form coalitions to achieve the goal, thus yielding reasonable generation of multirobot joint action plans.
Keyphrases
multirobot joint action simultaneous task execution demonstration abstract multirobot joint action plan action recognition hammer architecture spatio-temporal clustering algorithm novel approach exact moment individual action rescue task deployment single teacher multiple teacher numerous architecture demonstrated sequence central problem individual level desired behaviour reasonable generation intelligent robot system real robot group behaviour group behaviour segmentation little work experimental result unlabelled data simultaneous demonstration
