Movement primitives via optimization

We formalize the problem of adapting a demonstrated trajectory to a new start and goal configuration as an optimization problem over a Hilbert space of trajectories: minimize the distance between the demonstration and the new trajectory subject to the new end point constraints. We show that the comm...

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Bibliographic Details
Published inProceedings - IEEE International Conference on Robotics and Automation pp. 2339 - 2346
Main Authors Dragan, Anca D., Muelling, Katharina, Bagnell, J. Andrew, Srinivasa, Siddhartha S.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.05.2015
Subjects
Minimization
Optimization
Robots
Shock absorbers
Springs
Target tracking
Trajectory
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Summary:We formalize the problem of adapting a demonstrated trajectory to a new start and goal configuration as an optimization problem over a Hilbert space of trajectories: minimize the distance between the demonstration and the new trajectory subject to the new end point constraints. We show that the commonly used version of Dynamic Movement Primitives (DMPs) implement this minimization in the way they adapt demonstrations, for a particular choice of the Hilbert space norm. The generalization to arbitrary norms enables the robot to select a more appropriate norm for the task, as well as learn how to adapt the demonstration from the user. Our experiments show that this can significantly improve the robot's ability to accurately generalize the demonstration.
ISSN:1050-4729
DOI:10.1109/ICRA.2015.7139510