Benjamin Kuipers, Patrick Beeson, Joseph Modayil, and Jefferson
Provost. 2006.
Bootstrap learning of foundational representations.
Connection Science, 18(2): 145-158, 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.
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