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Adaptation and Robust Learning of Probabilistic Movement Primitives.

Authors :
Gomez-Gonzalez, Sebastian
Neumann, Gerhard
Scholkopf, Bernhard
Peters, Jan
Source :
IEEE Transactions on Robotics. Apr2020, Vol. 36 Issue 2, p366-379. 14p.
Publication Year :
2020

Abstract

Probabilistic representations of movement primitives open important new possibilities for machine learning in robotics. These representations are able to capture the variability of the demonstrations from a teacher as a probability distribution over trajectories, providing a sensible region of exploration and the ability to adapt to changes in the robot environment. However, to be able to capture variability and correlations between different joints, a probabilistic movement primitive requires the estimation of a larger number of parameters compared to their deterministic counterparts, which focus on modeling only the mean behavior. In this article, we make use of prior distributions over the parameters of a probabilistic movement primitive to make robust estimates of the parameters with few training instances. In addition, we introduce general purpose operators to adapt movement primitives in joint and task space. The proposed training method and adaptation operators are tested in a coffee preparation and in robot table tennis task. In the coffee preparation task we evaluate the generalization performance to changes in the location of the coffee grinder and brewing chamber in a target area, achieving the desired behavior after only two demonstrations. In the table tennis task we evaluate the hit and return rates, outperforming previous approaches while using fewer task specific heuristics. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
15523098
Volume :
36
Issue :
2
Database :
Academic Search Index
Journal :
IEEE Transactions on Robotics
Publication Type :
Academic Journal
Accession number :
142612638
Full Text :
https://doi.org/10.1109/TRO.2019.2937010