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MetareasoningThinking about Thinking$
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Michael T. Cox and Anita Raja

Print publication date: 2011

Print ISBN-13: 9780262014809

Published to MIT Press Scholarship Online: August 2013

DOI: 10.7551/mitpress/9780262014809.001.0001

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Robotic Models of Self

Robotic Models of Self

(p.283) 18 Robotic Models of Self

Justin Hart

Brian Scassellati

The MIT Press

This chapter presents a viewpoint that unifies a few subfields of robotics that until now were studied in isolation. By recasting the primary questions of these fields as part of the continual process for constructing an accurate model of self, it demonstrates that each of these questions can be characterized as part of a larger domain. Adaptive, self-taught models of self provide a framework for studying causal learning, tool use, kinematic analysis, and fault detection and recovery. While the study of these fields independently will continue to advance the state of the art, it is argued that the study of these as part of an integrated self-model will allow for even more fundamental insights into how to build useful, adaptive, and practical robotic systems and may cast light onto the underlying processes of self-identification that biological systems must also solve.

Keywords:   robots, robotics, self, causal learning, tool use, kinematic analysis, fault detection, recovery, self-identification

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