Imitation in animals and artifacts
Natural methods for robot task learning: instructive demonstrations, generalization and practice
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Human control for cooperating robot teams
Proceedings of the ACM/IEEE international conference on Human-robot interaction
Confidence-based policy learning from demonstration using Gaussian mixture models
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Understanding human intentions via hidden markov models in autonomous mobile robots
Proceedings of the 3rd ACM/IEEE international conference on Human robot interaction
Teaching multi-robot coordination using demonstration of communication and state sharing
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 3
Balancing Spectral Clustering for Segmenting Spatio-temporal Observations of Multi-agent Systems
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
A survey of robot learning from demonstration
Robotics and Autonomous Systems
Learning from demonstration with swarm hierarchies
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Multi Robot Learning by Demonstration
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 3
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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.