Clone Swarms: Learning to Predict and Control Multi-Robot Systems by Imitation

Siyu Zhou, Mariano J. Phielipp, Jorge A. Sefair, Sara I. Walker, Heni Ben Amor

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper, we propose SwarmNet - a neural network architecture that can learn to predict and imitate the behavior of an observed swarm of agents in a centralized manner. Tested on artificially generated swarm motion data, the network achieves high levels of prediction accuracy and imitation authenticity. We compare our model to previous approaches for modelling interaction systems and show how modifying components of other models gradually approaches the performance of ours. Finally, we also discuss an extension of SwarmNet that can deal with nondeterministic, noisy, and uncertain environments, as often found in robotics applications.

Original languageEnglish (US)
Title of host publication2019 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages4092-4099
Number of pages8
ISBN (Electronic)9781728140049
DOIs
StatePublished - Nov 2019
Event2019 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2019 - Macau, China
Duration: Nov 3 2019Nov 8 2019

Publication series

NameIEEE International Conference on Intelligent Robots and Systems
ISSN (Print)2153-0858
ISSN (Electronic)2153-0866

Conference

Conference2019 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2019
CountryChina
CityMacau
Period11/3/1911/8/19

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Software
  • Computer Vision and Pattern Recognition
  • Computer Science Applications

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  • Cite this

    Zhou, S., Phielipp, M. J., Sefair, J. A., Walker, S. I., & Amor, H. B. (2019). Clone Swarms: Learning to Predict and Control Multi-Robot Systems by Imitation. In 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2019 (pp. 4092-4099). [8967824] (IEEE International Conference on Intelligent Robots and Systems). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IROS40897.2019.8967824