Geometry-aware Dynamic Movement Primitives

Fares J. Abu-Dakka*, Ville Kyrki

*Corresponding author for this work

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

27 Citations (Scopus)
168 Downloads (Pure)


In many robot control problems, factors such as stiffness and damping matrices and manipulability ellipsoids are naturally represented as symmetric positive definite (SPD) matrices, which capture the specific geometric characteristics of those factors. Typical learned skill models such as dynamic movement primitives (DMPs) can not, however, be directly employed with quantities expressed as SPD matrices as they are limited to data in Euclidean space. In this paper, we propose a novel and mathematically principled framework that uses Riemannian metrics to reformulate DMPs such that the resulting formulation can operate with SPD data in the SPD manifold. Evaluation of the approach demonstrates that beneficial properties of DMPs such as change of the goal during operation apply also to the proposed formulation.
Original languageEnglish
Title of host publicationProceedings of the IEEE Conference on Robotics and Automation, ICRA 2020
Number of pages6
ISBN (Electronic)978-1-7281-7395-5
Publication statusPublished - 2020
MoE publication typeA4 Conference publication
EventIEEE International Conference on Robotics and Automation - Online, Paris, France
Duration: 31 May 202031 Aug 2020

Publication series

NameIEEE International Conference on Robotics and Automation
ISSN (Print)2152-4092
ISSN (Electronic)2379-9552


ConferenceIEEE International Conference on Robotics and Automation
Abbreviated titleICRA


  • Manifolds
  • Robots
  • Symmetric matrices
  • Standards
  • Ellipsoids
  • Switches
  • Measurement


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