Abstract
A hierarchical network for visuo-motor coordination is proposed. The hierarchical approach allows learning geometric models of realistic robots with six or more axes. The network consists of several one-dimensional subnetworks, which learn the coordinate transform and rotation axis for each joint. In our simulation, the network reduces the end-effector error of a 7-axis anthropomorphic robot and 20-axis robot below the visual error.
Cite
CITATION STYLE
Maël, E. (1996). A hierarchical network for learning robust models of kinematic chains. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 1112 LNCS, pp. 617–622). Springer Verlag. https://doi.org/10.1007/3-540-61510-5_105
Register to see more suggestions
Mendeley helps you to discover research relevant for your work.
