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Paper WeP212.3

Censi, Andrea (California Institute of Technology), Murray, Richard (California Institute of Technology)

Bootstrapping Bilinear Models of Robotic Sensorimotor Cascades

Scheduled for presentation during the Regular Sessions "Learning and Adaptive Systems II" (WeP212), Wednesday, May 11, 2011, 15:55−16:10, Room 5H

2011 IEEE International Conference on Robotics and Automation, May 9-13, 2011, Shanghai International Conference Center, Shanghai, China

This information is tentative and subject to change. Compiled on December 8, 2019

Keywords Learning and Adaptive Systems, Calibration and Identification, Visual Servoing

Abstract

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 sensors with bilinear dynamics, for which the derivative of the observations is 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 sensors (field sampler, camera, and range-finder), apparently very different from one another. It also allows a bootstrapping algorithm based on Hebbian learning, and a simple bioplausible control strategy. The convergence properties of learning and control are demonstrated with extensive simulations and by analytical arguments.

 

 

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