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Autonomous Robots
Erschienen in: Autonomous Robots 3/2018

15.07.2017

Using probabilistic movement primitives in robotics

verfasst von: Alexandros Paraschos, Christian Daniel, Jan Peters, Gerhard Neumann

Erschienen in: Autonomous Robots | Ausgabe 3/2018

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Abstract

Movement Primitives are a well-established paradigm for modular movement representation and generation. They provide a data-driven representation of movements and support generalization to novel situations, temporal modulation, sequencing of primitives and controllers for executing the primitive on physical systems. However, while many MP frameworks exhibit some of these properties, there is a need for a unified framework that implements all of them in a principled way. In this paper, we show that this goal can be achieved by using a probabilistic representation. Our approach models trajectory distributions learned from stochastic movements. Probabilistic operations, such as conditioning can be used to achieve generalization to novel situations or to combine and blend movements in a principled way. We derive a stochastic feedback controller that reproduces the encoded variability of the movement and the coupling of the degrees of freedom of the robot. We evaluate and compare our approach on several simulated and real robot scenarios.
Vorheriger Artikel Edge alignment-based visual–inertial fusion for tracking of aggressive motions
Nächster Artikel Integration of sliding mode based steering control and PSO based drive force control for a 4WS4WD vehicle

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Fußnoten
2
This prior variance profile can be just set to \(\alpha \varvec{I}\), where \(\alpha \) is a small constant and \(\varvec{I}\) is the identity matrix.
 
3
The third derivative of \(\varvec{\Psi }\) can be computed numerically.
 
4
If inverse dynamics control (Peters et al. 2008) is used for the robot, the system reduces to a linear system where the terms \(\varvec{A}_{t}\), \(\varvec{B}_{t}\) and \(\varvec{c}_{t}\) are constant in time.
 
5
As we multiply the noise by \(\varvec{B}{{\mathrm{dt}}}\), we need to divide the covariance \(\varvec{\Sigma }_{u}\) of the control noise \(\varvec{\epsilon }_{u}\) by \({{\mathrm{dt}}}\) to obtain this desired behavior.
 
6
The observation noise is omitted as it represents independent noise which is not used for predicting the next state.
 
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Metadaten
Titel
Using probabilistic movement primitives in robotics
verfasst von
Alexandros Paraschos
Christian Daniel
Jan Peters
Gerhard Neumann
Publikationsdatum
15.07.2017
Verlag
Springer US
Erschienen in
Autonomous Robots / Ausgabe 3/2018
Print ISSN: 0929-5593
Elektronische ISSN: 1573-7527
DOI
https://doi.org/10.1007/s10514-017-9648-7

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