Bootstrapping Vehicles: A Formal Approach to Unsupervised Sensorimotor Learning Based on Invariance

Bootstrapping Vehicles: A Formal Approach to Unsupervised Sensorimotor Learning Based on Invariance

Could a "brain in a jar" be able to control an unknown robotic body to which it is connected, and use it to achieve useful tasks, without any prior assumptions on the body's sensors and actuators? Other than of purely intellectual interest, this question is relevant to the medium-term...

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Bibliographic Details
Main Author: Censi, Andrea
Format: Others
Language:en
Published: 2013
Online Access:https://thesis.library.caltech.edu/7248/4/main_dissertation-caltech-oct28.pdf
Censi, Andrea (2013) Bootstrapping Vehicles: A Formal Approach to Unsupervised Sensorimotor Learning Based on Invariance. Dissertation (Ph.D.), California Institute of Technology. doi:10.7907/PWVS-2Q74. https://resolver.caltech.edu/CaltechTHESIS:10282012-082208075 <https://resolver.caltech.edu/CaltechTHESIS:10282012-082208075>

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https://thesis.library.caltech.edu/7248/4/main_dissertation-caltech-oct28.pdf
Censi, Andrea (2013) Bootstrapping Vehicles: A Formal Approach to Unsupervised Sensorimotor Learning Based on Invariance. Dissertation (Ph.D.), California Institute of Technology. doi:10.7907/PWVS-2Q74. https://resolver.caltech.edu/CaltechTHESIS:10282012-082208075 <https://resolver.caltech.edu/CaltechTHESIS:10282012-082208075>

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