Bootstrap learning of foundational representations
Benjamin Kuipers, Patrick Beeson, Joseph Modayil, and Jefferson Provost. Bootstrap learning of foundational representations. Connection Science, 18(2):145–158, June 2006.
Abstract
To be autonomous, intelligent robots must learn the foundations of commonsense knowledge from their own sensorimotor experience in the world. We describe four recent research results that contribute to a theory of how a robot learning agent can bootstrap from the blooming buzzing confusion of the pixel level to a higher-level ontology including distinctive states, places, objects, and actions. This is not a single learning problem, but a lattice of related learning tasks, each providing prerequisites for tasks to come later. Starting with completely uninterpreted sense and motor vectors, as well as an unknown environment, we show how a learning agent can separate the sense vector into modalities, learn the structure of individual modalities, learn natural primitives for the motor system, identify reliable relations between primitive actions and created sensory features, and can define useful control laws for homing and path-following. Building on this framework, we show how an agent can use to self-organizing maps to identify useful sensory featurs in the environment, and can learn effective hill-climbing control laws to define distinctive states in terms of thos features, and trajectoryfollowing control laws to move from one distinctive state to another. Moving on to place recognition, we show how an agent can combine unsupervised learning, map-learning, and supervised learning to achieve high-performance recognition of places from rich sensory input. And finally, we take the first steps toward learning an ontology of objects, showing tha a bootstrap learning robot can learn to individuate objects through motion, separating them from the static environment and from each other, and learning properties that will be useful for classification. These are four key steps in a much larger research enterprise that lays the foundation for human and robot commonsense knowledge.
BibTeX
@Article{Kuipers-connsci-06,
author = {Benjamin Kuipers and Patrick Beeson and Joseph
Modayil and Jefferson Provost},
title = {Bootstrap learning of foundational representations},
journal = {Connection Science},
year = 2006,
volume = 18,
number = 2,
pages = {145--158},
month = {June},
abstract = {To be autonomous, intelligent robots must learn the
foundations of commonsense knowledge from their own
sensorimotor experience in the world. We describe
four recent research results that contribute to a
theory of how a robot learning agent can bootstrap
from the blooming buzzing confusion of the pixel
level to a higher-level ontology including
distinctive states, places, objects, and
actions. This is not a single learning problem, but
a lattice of related learning tasks, each providing
prerequisites for tasks to come later. Starting with
completely uninterpreted sense and motor vectors, as
well as an unknown environment, we show how a
learning agent can separate the sense vector into
modalities, learn the structure of individual
modalities, learn natural primitives for the motor
system, identify reliable relations between
primitive actions and created sensory features, and
can define useful control laws for homing and
path-following. Building on this framework, we show
how an agent can use to self-organizing maps to
identify useful sensory featurs in the environment,
and can learn effective hill-climbing control laws
to define distinctive states in terms of thos
features, and trajectoryfollowing control laws to
move from one distinctive state to another. Moving
on to place recognition, we show how an agent can
combine unsupervised learning, map-learning, and
supervised learning to achieve high-performance
recognition of places from rich sensory input. And
finally, we take the first steps toward learning an
ontology of objects, showing tha a bootstrap
learning robot can learn to individuate objects
through motion, separating them from the static
environment and from each other, and learning
properties that will be useful for
classification. These are four key steps in a much
larger research enterprise that lays the foundation
for human and robot commonsense knowledge.},
bib2html_pubtype ={Journal},
bib2html_rescat ={Foundational Learning},
}