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Trajectory formation for imitation with nonlinear dynamical systems

2001

Conference Paper

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This article explores a new approach to learning by imitation and trajectory formation by representing movements as mixtures of nonlinear differential equations with well-defined attractor dynamics. An observed movement is approximated by finding a best fit of the mixture model to its data by a recursive least squares regression technique. In contrast to non-autonomous movement representations like splines, the resultant movement plan remains an autonomous set of nonlinear differential equations that forms a control policy which is robust to strong external perturbations and that can be modified by additional perceptual variables. This movement policy remains the same for a given target, regardless of the initial conditions, and can easily be re-used for new targets. We evaluate the trajectory formation system (TFS) in the context of a humanoid robot simulation that is part of the Virtual Trainer (VT) project, which aims at supervising rehabilitation exercises in stroke-patients. A typical rehabilitation exercise was collected with a Sarcos Sensuit, a device to record joint angular movement from human subjects, and approximated and reproduced with our imitation techniques. Our results demonstrate that multi-joint human movements can be encoded successfully, and that this system allows robust modifications of the movement policy through external variables.

Author(s): Ijspeert, A. and Nakanishi, J. and Schaal, S.
Book Title: IEEE International Conference on Intelligent Robots and Systems (IROS 2001)
Pages: 752-757
Year: 2001

Department(s): Autonomous Motion
Bibtex Type: Conference Paper (inproceedings)

Address: Weilea, Hawaii, Oct.29-Nov.3
Cross Ref: p1460
Note: clmc
URL: http://www-clmc.usc.edu/publications/I/ijspeert-IROS2001.pdf

BibTex

@inproceedings{Ijspeert_IICIRS_2001,
  title = {Trajectory formation for imitation with nonlinear dynamical systems},
  author = {Ijspeert, A. and Nakanishi, J. and Schaal, S.},
  booktitle = {IEEE International Conference on Intelligent Robots and Systems (IROS 2001)},
  pages = {752-757},
  address = {Weilea, Hawaii, Oct.29-Nov.3},
  year = {2001},
  note = {clmc},
  crossref = {p1460},
  url = {http://www-clmc.usc.edu/publications/I/ijspeert-IROS2001.pdf}
}