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Robust learning of arm trajectories through human demonstration

2001

Conference Paper

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We present a model, composed of hierarchy of artificial neural networks, for robot learning by demonstration. The model is implemented in a dynamic simulation of a 41 degrees of freedom humanoid for reproducing 3D human motion of the arm. Results show that the model requires few information about the desired trajectory and learns on-line the relevant features of movement. It can generalize across a small set of data to produce a qualitatively good reproduction of the demonstrated trajectory. Finally, it is shown that reproduction of the trajectory after learning is robust against perturbations.

Author(s): Billard, A. and Schaal, S.
Book Title: IEEE International Conference on Intelligent Robots and Systems (IROS 2001)
Year: 2001
Publisher: Piscataway, NJ: IEEE

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

Address: Maui, Hawaii, Oct.29-Nov.3
Cross Ref: p1462
Note: clmc
URL: http://www-clmc.usc.edu/publications/B/billard-IROS2001.pdf

BibTex

@inproceedings{Billard_IICIRS_2001,
  title = {Robust learning of arm trajectories through human demonstration},
  author = {Billard, A. and Schaal, S.},
  booktitle = {IEEE International Conference on Intelligent Robots and Systems (IROS 2001)},
  publisher = {Piscataway, NJ: IEEE},
  address = {Maui, Hawaii, Oct.29-Nov.3},
  year = {2001},
  note = {clmc},
  crossref = {p1462},
  url = {http://www-clmc.usc.edu/publications/B/billard-IROS2001.pdf}
}