Autonomous development of a grounded object ontology by a learning robot

Authors:
Joseph Modayil;Benjamin Kuipers
Affiliations:
Department of Computer Sciences, The University of Texas at Austin;Department of Computer Sciences, The University of Texas at Austin
Venue:
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Year:
2007

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Abstract

We describe how a physical robot can learn about objects from its own autonomous experience in the continuous world. The robot identifies statistical regularities that allow it to represent a physical object with a cluster of sensations that violate a static world model, track that cluster over time, extract percepts from that cluster, form concepts from similar percepts, and learn reliable actions that can be applied to objects. We present a formalism for representing the ontology for objects and actions, a learning algorithm, and the results of an evaluation with a physical robot.