Geometry-aware Dynamic Movement Primitives

Fares J. Abu-Dakka, Ville Kyrki

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


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 (US)
Title of host publication2020 IEEE International Conference on Robotics and Automation, ICRA 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9781728173955
StatePublished - May 2020
Event2020 IEEE International Conference on Robotics and Automation, ICRA 2020 - Paris, France
Duration: May 31 2020Aug 31 2020

Publication series

NameProceedings - IEEE International Conference on Robotics and Automation
ISSN (Print)1050-4729


Conference2020 IEEE International Conference on Robotics and Automation, ICRA 2020

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Artificial Intelligence
  • Electrical and Electronic Engineering


Dive into the research topics of 'Geometry-aware Dynamic Movement Primitives'. Together they form a unique fingerprint.

Cite this