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Dynamical Movement Primitives: Learning Attractor Models for Motor Behaviors




Nonlinear dynamical systems have been used in many disciplines to model complex behaviors, including biological motor control, robotics, perception, economics, traffic prediction, and neuroscience. While often the unexpected emergent behavior of nonlinear systems is the focus of investigations, it is of equal importance to create goal-directed behavior (e.g., stable locomotion from a system of coupled oscillators under perceptual guidance). Modeling goal-directed behavior with nonlinear systems is, however, rather difficult due to the parameter sensitivity of these systems, their complex phase transitions in response to subtle parameter changes, and the difficulty of analyzing and predicting their long-term behavior; intuition and time-consuming parameter tuning play a major role. This letter presents and reviews dynamical movement primitives, a line of research for modeling attractor behaviors of autonomous nonlinear dynamical systems with the help of statistical learning techniques. The essence of our approach is to start with a simple dynamical system, such as a set of linear differential equations, and transform those into a weakly nonlinear system with prescribed attractor dynamics by meansof a learnable autonomous forcing term. Both point attractors and limit cycle attractors of almost arbitrary complexity can be generated. We explain the design principle of our approach and evaluate its properties in several example applications in motor control and robotics.

Author(s): Ijspeert, A. and Nakanishi, J. and Pastor, P and Hoffmann, H. and Schaal, S.
Journal: Neural Computation
Number (issue): 25
Pages: 328-373
Year: 2013

Department(s): Autonomous Motion
Bibtex Type: Article (article)

Cross Ref: p2663
Note: clmc
URL: http://www-clmc.usc.edu/publications/I/ijspeert-NC2013.pdf


  title = {Dynamical Movement Primitives: Learning Attractor Models for Motor Behaviors},
  author = {Ijspeert, A. and Nakanishi, J. and Pastor, P and Hoffmann, H. and Schaal, S.},
  journal = {Neural  Computation},
  number = {25},
  pages = {328-373},
  year = {2013},
  note = {clmc},
  crossref = {p2663},
  url = {http://www-clmc.usc.edu/publications/I/ijspeert-NC2013.pdf}