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Bootstrapping bilinear models of robotic sensorimotor cascades

Censi, Andrea and Murray, Richard M. (2010) Bootstrapping bilinear models of robotic sensorimotor cascades. California Institute of Technology , Pasadena, CA. (Unpublished)

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We consider the bootstrapping problem, which consists in learning a model of the agent's sensors and actuators starting from zero prior information, and we take the problem of servoing as a cross-modal task to validate the learned models. We study the class of bilinear dynamics sensors, in which the derivative of the observations are a bilinear form of the control commands and the observations themselves. This class of models is simple yet general enough to represent the main phenomena of three representative robotics sensors (field sampler, camera, and range-finder), apparently very different from one another. It also allows a bootstrapping algorithm based on hebbian learning, and that leads to a simple and bioplausible control strategy. The convergence properties of learning and control are demonstrated with extensive simulations and by analytical arguments.

Item Type:Report or Paper (Technical Report)
Murray, Richard M.0000-0002-5785-7481
Group:Control and Dynamical Systems Technical Reports
Record Number:CaltechCDSTR:2010.003a
Persistent URL:
Usage Policy:You are granted permission for individual, educational, research and non-commercial reproduction, distribution, display and performance of this work in any format.
ID Code:28142
Deposited By: Imported from CaltechCDSTR
Deposited On:20 Sep 2010
Last Modified:18 Mar 2015 22:58

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