Title
Bootstrap Learning Of Foundational Representations
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 self-organizing maps to identify useful sensory features in the environment, and can learn effective hill-climbing control laws to define distinctive states in terms of those features, and trajectory-following 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. Finally, we take the first steps toward learning an ontology of objects, showing that a bootstrap learning robot can learn to individuate objects through motion, separating them from the static environment and from each other, and can learn properties useful for classification. These are four key steps in a larger research enterprise on the foundations of human and robot commonsense knowledge.
Year
DOI
Venue
2006
10.1080/09540090600768484
CONNECTION SCIENCE
Keywords
Field
DocType
bootstrap learning, ontology learning, spatial learning, learning places, objects, actions
Robot learning,Modalities,Ontology,Commonsense knowledge,Computer science,Supervised learning,Unsupervised learning,Artificial intelligence,Ontology learning,Bootstrapping (electronics),Machine learning
Journal
Volume
Issue
ISSN
18
2
0954-0091
Citations 
PageRank 
References 
28
1.97
13
Authors
4
Name
Order
Citations
PageRank
Benjamin Kuipers14111875.19
Patrick Beeson217712.66
Joseph Modayil340329.02
Jefferson Provost4524.63