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Robust learning of arm trajectories through human demonstration
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.
@inproceedings{Billard_IICIRS_2001, title = {Robust learning of arm trajectories through human demonstration}, booktitle = {IEEE International Conference on Intelligent Robots and Systems (IROS 2001)}, abstract = {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.}, publisher = {Piscataway, NJ: IEEE}, address = {Maui, Hawaii, Oct.29-Nov.3}, year = {2001}, note = {clmc}, slug = {billard_iicirs_2001}, author = {Billard, A. and Schaal, S.}, crossref = {p1462}, url = {http://www-clmc.usc.edu/publications/B/billard-IROS2001.pdf} }