Roles of Knowledge in Motor Learning

Item

Title
en_US Roles of Knowledge in Motor Learning
Creator
en_US Atkeson, Christopher Granger
Date
2004-10-20T20:02:50Z
Date Available
2004-10-20T20:02:50Z
Date Issued
en_US 1987-02-01
Identifier
en_US AITR-942
uri
http://hdl.handle.net/1721.1/6858
Abstract
en_US The goal of this thesis is to apply the computational approach to motor learning, i.e., describe the constraints that enable performance improvement with experience and also the constraints that must be satisfied by a motor learning system, describe what is being computed in order to achieve learning, and why it is being computed. The particular tasks used to assess motor learning are loaded and unloaded free arm movement, and the thesis includes work on rigid body load estimation, arm model estimation, optimal filtering for model parameter estimation, and trajectory learning from practice. Learning algorithms have been developed and implemented in the context of robot arm control. The thesis demonstrates some of the roles of knowledge in learning. Powerful generalizations can be made on the basis of knowledge of system structure, as is demonstrated in the load and arm model estimation algorithms. Improving the performance of parameter estimation algorithms used in learning involves knowledge of the measurement noise characteristics, as is shown in the derivation of optimal filters. Using trajectory errors to correct commands requires knowledge of how command errors are transformed into performance errors, i.e., an accurate model of the dynamics of the controlled system, as is demonstrated in the trajectory learning work. The performance demonstrated by the algorithms developed in this thesis should be compared with algorithms that use less knowledge, such as table based schemes to learn arm dynamics, previous single trajectory learning algorithms, and much of traditional adaptive control.
Extent
en_US 154 p.
10983236 bytes
7499252 bytes
Format
application/postscript
application/pdf
Language
en_US
Relation
en_US AITR-942
Subject
en_US motor control
en_US motor learning
en_US learning
en_US practice
en_US robotics
en_US ssystem identification
Item sets
AI Technical Reports (1964 - 2004)