Using probabilistic movement primitives in robotics

Paraschos, Alexandros and Daniel, Christian and Peters, Jan and Neumann, Gerhard (2018) Using probabilistic movement primitives in robotics. Autonomous Robots . ISSN 0929-5593

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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 uni-
fied 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.

Keywords:movement primitives, robotics, movement skills
Subjects:H Engineering > H671 Robotics
G Mathematical and Computer Sciences > G760 Machine Learning
Divisions:College of Science > School of Computer Science
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ID Code:27883
Deposited On:18 Jul 2017 14:52

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