Learning multirobot joint action plans from simultaneous task execution demonstrations

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 w...

• Type: Conference
• Year: 2010
• Where: ATAL
• Authors: Murilo Fernandes Martins, Yiannis Demiris
• Keywords: ATAL 2010, Intelligent Agents, Joint Action Plans, Multirobot Joint Action, Robots