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Learning Action Primitives

  • Dana Kulić
  • Danica Kragic
  • Volker Krüger

Abstract

The use of action primitives plays an important role in modeling actions. Action primitives are motivated not only by neurobiological findings, they also allow an efficient and effective action modeling from an information-theoretic viewpoint. Different approaches for modeling action primitives have been proposed. This chapter overviews the recent approaches for learning and modeling action primitives for human and robot action and describes common approaches such as stochastic methods and dynamical systems approaches. Active research questions in the field are introduced, including temporal segmentation, dimensionality reduction, and the integration of action primitives into complex behaviors.

Keywords

Dimensionality Reduction Hide State Mirror Neuron System Dynamical System Approach Motion Primitive 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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© Springer-Verlag London Limited 2011

Authors and Affiliations

  1. 1.University of WaterlooWaterlooCanada
  2. 2.Centre for Autonomous SystemsRoyal Institute of Technology – KTHStockholmSweden
  3. 3.Aalborg University CopenhagenBallerupDenmark

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