Coordinate Transformation Learning of Hand Position Feedback Controller by Using Change of Position Error Norm
Abstract
In order to grasp an object, we need to solve the inverse kine(cid:173) matics problem, i.e., the coordinate transformation from the visual coordinates to the joint angle vector coordinates of the arm. Al(cid:173) though several models of coordinate transformation learning have been proposed, they suffer from a number of drawbacks. In human motion control, the learning of the hand position error feedback controller in the inverse kinematics solver is important. This paper proposes a novel model of the coordinate transformation learning of the human visual feedback controller that uses the change of the joint angle vector and the corresponding change of the square of the hand position error norm. The feasibility of the proposed model is illustrated using numerical simulations.
Cite
Text
Oyama and Tachi. "Coordinate Transformation Learning of Hand Position Feedback Controller by Using Change of Position Error Norm." Neural Information Processing Systems, 1998.Markdown
[Oyama and Tachi. "Coordinate Transformation Learning of Hand Position Feedback Controller by Using Change of Position Error Norm." Neural Information Processing Systems, 1998.](https://mlanthology.org/neurips/1998/oyama1998neurips-coordinate/)BibTeX
@inproceedings{oyama1998neurips-coordinate,
title = {{Coordinate Transformation Learning of Hand Position Feedback Controller by Using Change of Position Error Norm}},
author = {Oyama, Eimei and Tachi, Susumu},
booktitle = {Neural Information Processing Systems},
year = {1998},
pages = {1038-1044},
url = {https://mlanthology.org/neurips/1998/oyama1998neurips-coordinate/}
}