Learning movement synchronization in multi-component robotic systems

Mohammad Thabet, Alberto Montebelli, Ville Kyrki

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

1 Citation (Scopus)


Imitation learning of tasks in multi-component robotic systems requires capturing concurrency and synchronization requirements in addition to task structure. Learning time-critical tasks depends furthermore on the ability to model temporal elements in demonstrations. This paper proposes a modeling framework based on Petri nets capable of modeling these aspects in a programming by demonstration context. In the proposed approach, models of tasks are constructed from segmented demonstrations as task Petri nets, which can be executed as discrete controllers for reproduction. We present algorithms that automatically construct models from demonstrations, showing how elements of time-critical tasks can be mapped into task Petri net elements. The approach is validated by an experiment in which a robot plays a musical passage on a keyboard.

Original languageEnglish
Title of host publication2016 IEEE International Conference on Robotics and Automation, ICRA 2016
Number of pages8
ISBN (Electronic)9781467380263
Publication statusPublished - 8 Jun 2016
MoE publication typeA4 Article in a conference publication
EventIEEE International Conference on Robotics and Automation - Stockholm, Sweden
Duration: 16 May 201621 May 2016


ConferenceIEEE International Conference on Robotics and Automation
Abbreviated titleICRA


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