Using probabilistic movement primitives in robotics

Paraschos, Alexandros, Daniel, Christian, Peters, Jan and Neumann, Gerhard (2018) Using probabilistic movement primitives in robotics. Autonomous Robots, 42 (3). pp. 529-551. ISSN 0929-5593

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Using probabilistic movement primitives in robotics
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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.

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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