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Learning movement synchronization in multi-component robotic systems

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

Details

Original languageEnglish
Title of host publication2016 IEEE International Conference on Robotics and Automation (ICRA)
PublisherIEEE
Pages249-256
Number of pages8
ISBN (Print)9781467380263
DOIs
Publication statusPublished - 8 Jun 2016
Publication typeA4 Article in a conference publication
EventIEEE International Conference on Robotics and Automation -
Duration: 1 Jan 19001 Jan 2000

Publication series

Name
ISSN (Print)2152-4092

Conference

ConferenceIEEE International Conference on Robotics and Automation
Period1/01/001/01/00

Abstract

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.