Efficient Model Identification for Tensegrity Locomotion

Shaojun Zhu, David Surovik, Kostas Bekris, Abdeslam Boularias

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

1 Citation (Scopus)

Abstract

This paper aims to identify in a practical manner unknown physical parameters, such as mechanical models of actuated robot links, which are critical in dynamical robotic tasks. Key features include the use of an off-the-shelf physics engine and the Bayesian optimization framework. The task being considered is locomotion with a high-dimensional, compliant Tensegrity robot. A key insight, in this case, is the need to project the space of models into an appropriate lower dimensional space for time efficiency. Comparisons with alternatives indicate that the proposed method can identify the parameters more accurately within the given time budget, which also results in more precise locomotion control.

Original languageEnglish (US)
Title of host publication2018 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2985-2990
Number of pages6
ISBN (Electronic)9781538680940
DOIs
StatePublished - Dec 27 2018
Event2018 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2018 - Madrid, Spain
Duration: Oct 1 2018Oct 5 2018

Publication series

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

Conference

Conference2018 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2018
CountrySpain
CityMadrid
Period10/1/1810/5/18

Fingerprint

Identification (control systems)
Robots
Robotics
Physics
Engines

All Science Journal Classification (ASJC) codes

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

Cite this

Zhu, S., Surovik, D., Bekris, K., & Boularias, A. (2018). Efficient Model Identification for Tensegrity Locomotion. In 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2018 (pp. 2985-2990). [8594425] (IEEE International Conference on Intelligent Robots and Systems). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IROS.2018.8594425
Zhu, Shaojun ; Surovik, David ; Bekris, Kostas ; Boularias, Abdeslam. / Efficient Model Identification for Tensegrity Locomotion. 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2018. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 2985-2990 (IEEE International Conference on Intelligent Robots and Systems).
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abstract = "This paper aims to identify in a practical manner unknown physical parameters, such as mechanical models of actuated robot links, which are critical in dynamical robotic tasks. Key features include the use of an off-the-shelf physics engine and the Bayesian optimization framework. The task being considered is locomotion with a high-dimensional, compliant Tensegrity robot. A key insight, in this case, is the need to project the space of models into an appropriate lower dimensional space for time efficiency. Comparisons with alternatives indicate that the proposed method can identify the parameters more accurately within the given time budget, which also results in more precise locomotion control.",
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Zhu, S, Surovik, D, Bekris, K & Boularias, A 2018, Efficient Model Identification for Tensegrity Locomotion. in 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2018., 8594425, IEEE International Conference on Intelligent Robots and Systems, Institute of Electrical and Electronics Engineers Inc., pp. 2985-2990, 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2018, Madrid, Spain, 10/1/18. https://doi.org/10.1109/IROS.2018.8594425

Efficient Model Identification for Tensegrity Locomotion. / Zhu, Shaojun; Surovik, David; Bekris, Kostas; Boularias, Abdeslam.

2018 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2018. Institute of Electrical and Electronics Engineers Inc., 2018. p. 2985-2990 8594425 (IEEE International Conference on Intelligent Robots and Systems).

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

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Zhu S, Surovik D, Bekris K, Boularias A. Efficient Model Identification for Tensegrity Locomotion. In 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2018. Institute of Electrical and Electronics Engineers Inc. 2018. p. 2985-2990. 8594425. (IEEE International Conference on Intelligent Robots and Systems). https://doi.org/10.1109/IROS.2018.8594425