Learning multirobot joint action plans from simultaneous task execution demonstrations.
File(s)
Accepted version
Author(s)
Martins, Murilo Fernandes
Demiris, Yiannis
Type
Conference Paper
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.
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.
Date Issued
2010-05-05
Citation
2010, 1, pp.931-938
ISBN
978-0-9826571-1-9
Publisher
ACM
Start Page
931
End Page
938
Volume
1
Copyright Statement
© 2010, International Foundation for Autonomous Agents and Multiagent Systems.
Description
14.01.14 KB. Ok to add accepted version to spiral, IFAAMS have given permission.
Identifier
http://dl.acm.org/citation.cfm?id=1838206.1838331
Source
International Conference on Autonomous Agents and Multiagent Systems
Publication Status
Published
Publisher URL
Coverage Spatial
Toronto, Canada