Using probabilistic movement primitives in robotics

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...

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Bibliographic Details
Published in:Autonomous robots Vol. 42; no. 3; pp. 529 - 551
Main Authors: Paraschos, Alexandros, Daniel, Christian, Peters, Jan, Neumann, Gerhard
Format: Journal Article
Language:English
Published: New York Springer US 01.03.2018
Springer Nature B.V
Subjects:
Artificial Intelligence
Computer Imaging
Computer simulation
Control
Engineering
Feedback control
Mechatronics
Pattern Recognition and Graphics
Probability theory
Representations
Robotics
Robotics and Automation
Robots
Vision
Control
Movement primitives
Trajectory representation
Imitation learning
Robotics
ISSN:0929-5593, 1573-7527
Online Access:Get full text
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Summary: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.
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ISSN:0929-5593
1573-7527
DOI:10.1007/s10514-017-9648-7