Towards Reversible Dynamic Movement Primitives

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In this paper we present an initial approach towards reversible robot movement primitives. Our approach is a modification of Dynamic Movement Primitives (DMPs), a widely used framework for robot learning from demonstration. DMPs are based on dynamical systems to guarantee properties such as convergence to a goal state, robustness to perturbation, and the ability to generalize to other goal states. Yet a main limitation of their original formulation is that they do not allow for movements to be reversed. Thus, to execute the same task forwards and backwards would mean to learn two separate primitives. We propose to replace the transformation system in DMPs with the Logistic Differential Equation (LDE), a known time-reversible non-linear system. Similarly to the original DMP formulation, our system's temporal evolution is controlled by a phase system, which in our case is derived from the LDE to guarantee reversibility. We evaluate our approach experimentally with demonstration data from a real robot assembly task, and show comparable properties to those of the original DMP system.
Original languageEnglish
Title of host publication2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Publication date2019
Article number8968270
ISBN (Print)978-1-7281-4005-6
ISBN (Electronic)978-1-7281-4004-9
Publication statusPublished - 2019
Event 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) - Macau, China
Duration: 3. Nov 20198. Nov 2019


Conference 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
SeriesI E E E International Conference on Intelligent Robots and Systems. Proceedings

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