Probabilistic movement primitives

Paraschos, A. and Daniel, C. and Peters, J. and Neumann, G. (2013) Probabilistic movement primitives. In: Advances in Neural Information Processing Systems, (NIPS), 5 - 10 December 2013, Harrahs and Harveys, Lake Tahoe.

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Item Type:Conference or Workshop contribution (Paper)
Item Status:Live Archive

Abstract

Movement Primitives (MP) are a well-established approach for representing modular
and re-usable robot movement generators. Many state-of-the-art robot learning
successes are based MPs, due to their compact representation of the inherently
continuous and high dimensional robot movements. A major goal in robot learning
is to combine multiple MPs as building blocks in a modular control architecture
to solve complex tasks. To this effect, a MP representation has to allow for
blending between motions, adapting to altered task variables, and co-activating
multiple MPs in parallel. We present a probabilistic formulation of the MP concept
that maintains a distribution over trajectories. Our probabilistic approach
allows for the derivation of new operations which are essential for implementing
all aforementioned properties in one framework. In order to use such a trajectory
distribution for robot movement control, we analytically derive a stochastic feedback
controller which reproduces the given trajectory distribution. We evaluate
and compare our approach to existing methods on several simulated as well as
real robot scenarios.

Keywords:Movement Primitives, Robotics, Movement Generation
Subjects:G Mathematical and Computer Sciences > G760 Machine Learning
H Engineering > H671 Robotics
Divisions:College of Science > School of Computer Science
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ID Code:25785
Deposited On:29 Mar 2017 07:37

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