article

Dynamical movement primitives: Learning attractor models for motor behaviors

Authors:

Auke Jan Ijspeert,

Heiko Hoffmann,

Stefan SchaalAuthors Info & Claims

Neural Computation, Volume 25, Issue 2

Pages 328 - 373

https://doi.org/10.1162/NECO_a_00393

Published: 01 February 2013 Publication History

Abstract

Nonlinear dynamical systems have been used in many disciplines to model complex behaviors, including biological motor control, robotics, perception, economics, traffic prediction, and neuroscience. While often the unexpected emergent behavior of nonlinear systems is the focus of investigations, it is of equal importance to create goal-directed behavior e.g., stable locomotion from a system of coupled oscillators under perceptual guidance. Modeling goal-directed behavior with nonlinear systems is, however, rather difficult due to the parameter sensitivity of these systems, their complex phase transitions in response to subtle parameter changes, and the difficulty of analyzing and predicting their long-term behavior; intuition and time-consuming parameter tuning play a major role. This letter presents and reviews dynamical movement primitives, a line of research for modeling attractor behaviors of autonomous nonlinear dynamical systems with the help of statistical learning techniques. The essence of our approach is to start with a simple dynamical system, such as a set of linear differential equations, and transform those into a weakly nonlinear system with prescribed attractor dynamics by means of a learnable autonomous forcing term. Both point attractors and limit cycle attractors of almost arbitrary complexity can be generated. We explain the design principle of our approach and evaluate its properties in several example applications in motor control and robotics.

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Cited By

Nah MLachner JHogan N(2024)Robot control based on motor primitivesInternational Journal of Robotics Research10.1177/0278364924125878243:12(1959-1991)Online publication date: 1-Oct-2024
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Duan ABatzianoulis ICamoriano RRosasco LPucci DBillard A(2024)A structured prediction approach for robot imitation learningInternational Journal of Robotics Research10.1177/0278364923120465643:2(113-133)Online publication date: 1-Feb-2024
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Chen YMiao SChen GYe JFu CLiang BSong SLi X(2024)Learning to Assist Different Wearers in Multitasks: Efficient and Individualized Human-in-the-Loop Adaptation Framework for Lower-Limb ExoskeletonIEEE Transactions on Robotics10.1109/TRO.2024.346876840(4699-4718)Online publication date: 1-Jan-2024
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cover image Neural Computation

Neural Computation Volume 25, Issue 2

February 2013

278 pages

ISSN:0899-7667

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MIT Press

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Nah MLachner JHogan N(2024)Robot control based on motor primitivesInternational Journal of Robotics Research10.1177/0278364924125878243:12(1959-1991)Online publication date: 1-Oct-2024
https://dl.acm.org/doi/10.1177/02783649241258782
Duan ABatzianoulis ICamoriano RRosasco LPucci DBillard A(2024)A structured prediction approach for robot imitation learningInternational Journal of Robotics Research10.1177/0278364923120465643:2(113-133)Online publication date: 1-Feb-2024
https://dl.acm.org/doi/10.1177/02783649231204656
Chen YMiao SChen GYe JFu CLiang BSong SLi X(2024)Learning to Assist Different Wearers in Multitasks: Efficient and Individualized Human-in-the-Loop Adaptation Framework for Lower-Limb ExoskeletonIEEE Transactions on Robotics10.1109/TRO.2024.346876840(4699-4718)Online publication date: 1-Jan-2024
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Lee Y(2024)MMP++: Motion Manifold Primitives With Parametric Curve ModelsIEEE Transactions on Robotics10.1109/TRO.2024.344406840(3950-3963)Online publication date: 1-Jan-2024
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Ruan SLiu WWang XMeng XChirikjian G(2024)PRIMP: PRobabilistically-Informed Motion Primitives for Efficient Affordance Learning From DemonstrationIEEE Transactions on Robotics10.1109/TRO.2024.339005240(2868-2887)Online publication date: 16-Apr-2024
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