Optimal Movement Primitives
Abstract
The theory of Optimal Unsupervised Motor Learning shows how a network can discover a reduced-order controller for an unknown nonlinear system by representing only the most significant modes. Here, I extend the theory to apply to command sequences, so that the most significant components discovered by the network corre(cid:173) spond to motion "primitives". Combinations of these primitives can be used to produce a wide variety of different movements. I demonstrate applications to human handwriting decomposition and synthesis, as well as to the analysis of electrophysiological experiments on movements resulting from stimulation of the frog spinal cord.
Cite
Text
Sanger. "Optimal Movement Primitives." Neural Information Processing Systems, 1994.Markdown
[Sanger. "Optimal Movement Primitives." Neural Information Processing Systems, 1994.](https://mlanthology.org/neurips/1994/sanger1994neurips-optimal/)BibTeX
@inproceedings{sanger1994neurips-optimal,
title = {{Optimal Movement Primitives}},
author = {Sanger, Terence D.},
booktitle = {Neural Information Processing Systems},
year = {1994},
pages = {1023-1030},
url = {https://mlanthology.org/neurips/1994/sanger1994neurips-optimal/}
}