Learning Global Direct Inverse Kinematics
Abstract
We introduce and demonstrate a bootstrap method for construction of an in(cid:173) verse function for the robot kinematic mapping using only sample configuration(cid:173) space/workspace data. Unsupervised learning (clustering) techniques are used on pre-image neighborhoods in order to learn to partition the configuration space into subsets over which the kinematic mapping is invertible. Supervised leam(cid:173) ing is then used separately on each of the partitions to approximate the inverse function. The ill-posed inverse kinematics function is thereby regularized, and a globa1 inverse kinematics solution for the wristless Puma manipulator is devel(cid:173) oped.
Cite
Text
DeMers and Kreutz-Delgado. "Learning Global Direct Inverse Kinematics." Neural Information Processing Systems, 1991.Markdown
[DeMers and Kreutz-Delgado. "Learning Global Direct Inverse Kinematics." Neural Information Processing Systems, 1991.](https://mlanthology.org/neurips/1991/demers1991neurips-learning/)BibTeX
@inproceedings{demers1991neurips-learning,
title = {{Learning Global Direct Inverse Kinematics}},
author = {DeMers, David and Kreutz-Delgado, Kenneth},
booktitle = {Neural Information Processing Systems},
year = {1991},
pages = {589-595},
url = {https://mlanthology.org/neurips/1991/demers1991neurips-learning/}
}