Jörg A. Walter
In this paper we discuss the "Parameterized Self-organizing Maps" (PSOM) as a learning method for rapidly creating high-dimensional, continuous mappings. By making use of available topological information the PSOM shows excellent generalization capabilities from a small set of training data. Unlike most other existing approaches that are limited to the representation of a input-output mappings, the PSOM provides as an important generalization a flexibly usable, continuous associate memory. This allows to represent several related mappings -- coexisting in a single and coherent framework.
Task specifications for redundant manipulators often leave the problem of picking one action from a subspace of possible alternatives. The PSOM approach offers a flexible and compact form to select from various constraint and target functions previously associated.
We present application results for learning several kinematic relations of a hydraulic robot finger in a single PSOM module. Based on only 27 data points, the PSOM learns the inverse kinematic with a mean positioning accuracy of 1% of the entire workspace. Another PSOM learns various ways to resolve the redundancy problem for positioning a 4 DOF manipulator.
(6 pages, Postscript 199 kbytes gzip)