THIS PAGE INCORPORATES CASCADING STYLE SHEET! TO VIEW PAGE CORRECTLY PLEASE UPDATE TO A CSS COMPLIANT BROWSER
 |
Robot Skill Learning and the Effect of Basis Function Choice |
pdf 428 KB
ps.gz 163 KB
ps 514 KB
@TechReport{Scheider:TR1992,
author = {J.G. Schneider and Christopher M. Brown},
title = {Robot Skill Learning and the Effect of Basis Function Choice},
institution = {Comp. Sci. Dept. University of Rochester},
year = {1992},
number = {437},
address = {Rochester NY},
month = {September},
abstract = {
We present a computational, constructive theory of tunable, open loop trajectory
skills. A skill is a controller whose outputs achieve any task in a space
characterized by n parameters, n > 1. Throwing a ball at a target is a 3-
dimensional task if the target may be anywhere within a 3-dimensional volume.
Repetitous pick and place tasks are zero-dimensional, and thus not skills. Skills
are performed open loop for speed reasons: we assume the entire command sequence
is generated before any feedback can become available. We do not assume prior
knowledge of plant or task models, so skills must be at least partly learned. A
skill output is a vector of values---in our work so far it is generated as the
sum of a base vector and a weighted change vector whose weight accomplishes the
tuning. Learning consists of a search for the best set of base and change vectors.
An interpretation process maps skill outputs into sequences of commands for the
plant by using basis functions (given a priori in this paper). The basis functions
may be arbitrarily complex. We claim that appropriate basis functions can speed up
the learning process and overcome the limitations of the linear trajectory tuning
algorithm. This report describes a skill learning algorithm and experiments done
with various basis functions and control methods for a one-dimensional throwing
task. It concludes with a discussion of future work in learning basis functions,
higher dimensional tasks, and comparisons against common learning and control
algorithms. }
}