Abstract
This thesis is an important advance in making robots more useable.
It develops a task acquisition system which demonstrates the
feasibility of constructing new programs just from the user leading
a robot. The result is ETAR, for Example-based Task Acquisition
in Robots, and has been implemented on an Excalibur robot. Any
person, who can do a task with the common direct lead mechanism on
industrial manipulators, can designate it to the robot through ETAR.
Thus, ETAR is an alternative to robot programming. The acquired
procedures are not only repeated sequences, but standard assembly
tasks such as widget construction and block stacking--tasks with
loops, branches, and variables. ETAR is a prototypical machine learning
system which begins from user examples on a real robot, requires
minimal background knowledge, learns inductively, and generates the task
description with the aid of a focussing mechanism. The focussing mechanism
forces ETAR to concentrate on important domain objects, thus
eliminating useless steps, determining a symbolic translation for the
task, finding loops, introducing branches, and inducing functions to
merge examples into one general program.
Additionally, this thesis contributes to low level robotics. It provides
unpublished kinematics for the Excalibur robot. Furthermore, it offers
a unique, intuitive introduction to quaternions and describes how they
rotate vectors and interpolate orientations more efficiently than matrices.
Quaternions are used to obtain straight line motion for the Excalibur
robot. Implementing kinematics and motion interpolation was a preliminary
requirement to the learning algorithm.
Notes
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