Robot task learning from demonstration using Petri nets

The ability to learn is essential for robots if they are to function within human environments. Learning requires an understanding of the underlying structure of what has been observed. This paper proposes a learning method that automatically creates Petri nets from observation of human demonstrations to model the underlying structure of tasks. The Petri net can be learned via a single or multiple demonstrations. The learned Petri nets are capable of generating action sequences to allow a robot to imitate the task. The proposed model also allows for generalization and variations in performing the task. The proposed method is tested on demonstrations of block stacking tasks and verified through robot imitation of the tasks in simulation and in physical experiments.