Robust learning of arm trajectories through human demonstration
2001
Conference Paper
am
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}, doi = {}, crossref = {p1462}, url = {http://www-clmc.usc.edu/publications/B/billard-IROS2001.pdf} } |