Learning an internal representation of the end-effector configuration space
Machine Learning
2018-10-05 v1 Robotics
Machine Learning
Abstract
Current machine learning techniques proposed to automatically discover a robot kinematics usually rely on a priori information about the robot's structure, sensors properties or end-effector position. This paper proposes a method to estimate a certain aspect of the forward kinematics model with no such information. An internal representation of the end-effector configuration is generated from unstructured proprioceptive and exteroceptive data flow under very limited assumptions. A mapping from the proprioceptive space to this representational space can then be used to control the robot.
Cite
@article{arxiv.1810.01866,
title = {Learning an internal representation of the end-effector configuration space},
author = {Alban Laflaquière and Alexander V. Terekhov and Bruno Gas and J. Kevin O'Regan},
journal= {arXiv preprint arXiv:1810.01866},
year = {2018}
}
Comments
6 pages, 3 figures, IROS 2013