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Autonomous Motion Conference Paper 2004

A framework for learning biped locomotion with dynamic movement primitives

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Autonomous Motion
Stefan Schaal
  • Director

This article summarizes our framework for learning biped locomotion using dynamical movement primitives based on nonlinear oscillators. Our ultimate goal is to establish a design principle of a controller in order to achieve natural human-like locomotion. We suggest dynamical movement primitives as a central pattern generator (CPG) of a biped robot, an approach we have previously proposed for learning and encoding complex human movements. Demonstrated trajectories are learned through movement primitives by locally weighted regression, and the frequency of the learned trajectories is adjusted automatically by a frequency adaptation algorithm based on phase resetting and entrainment of coupled oscillators. Numerical simulations and experimental implementation on a physical robot demonstrate the effectiveness of the proposed locomotion controller. Furthermore, we demonstrate that phase resetting contributes to robustness against external perturbations and environmental changes by numerical simulations and experiments.

Author(s): Nakanishi, J. and Morimoto, J. and Endo, G. and Cheng, G. and Schaal, S. and Kawato, M.
Book Title: IEEE-RAS/RSJ International Conference on Humanoid Robots (Humanoids 2004)
Year: 2004
Publisher: IEEE
Bibtex Type: Conference Paper (inproceedings)
Address: Los Angeles, CA: Nov.10-12, Santa Monica, CA
URL: http://www-clmc.usc.edu/publications/N/nakanishi-ICHR2004.pdf
Cross Ref: p1990
Electronic Archiving: grant_archive
Note: clmc

BibTex 

@inproceedings{Nakanishi_IICHR_2004,
  title = {A framework for learning biped locomotion with dynamic movement primitives},
  booktitle = {IEEE-RAS/RSJ International Conference on Humanoid Robots (Humanoids 2004)},
  abstract = {This article summarizes our framework for learning biped locomotion using dynamical movement primitives based on nonlinear oscillators. Our ultimate goal is to establish a design principle of a controller in order to achieve natural human-like locomotion. We suggest dynamical movement primitives as a central pattern generator (CPG) of a biped robot, an approach we have previously proposed for learning and encoding complex human movements. Demonstrated trajectories are learned through movement primitives by locally weighted regression, and the frequency of the learned trajectories is adjusted automatically by a frequency adaptation algorithm based on phase resetting and entrainment of coupled oscillators. Numerical simulations and experimental implementation on a physical robot demonstrate the effectiveness of the proposed locomotion controller. Furthermore, we demonstrate that phase resetting contributes to robustness against external perturbations and environmental changes by numerical simulations and experiments. },
  publisher = {IEEE},
  address = {Los Angeles, CA: Nov.10-12, Santa Monica, CA},
  year = {2004},
  note = {clmc},
  slug = {nakanishi_iichr_2004},
  author = {Nakanishi, J. and Morimoto, J. and Endo, G. and Cheng, G. and Schaal, S. and Kawato, M.},
  crossref = {p1990},
  url = {http://www-clmc.usc.edu/publications/N/nakanishi-ICHR2004.pdf}
}