research product . 2013

Probabilistic movement primitives

Paraschos, A.; Daniel, C.; Peters, J.; Neumann, G.;
Open Access English
  • Published: 05 Dec 2013
  • Country: United Kingdom
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 o...
Subjects
arXiv: Computer Science::Robotics
free text keywords: G760 Machine Learning, H671 Robotics
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