Name
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. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/ICRA.2011.5979844 | ICRA |
Keywords | Field | DocType |
bilinear form,tensile stress,convergence,hebbian learning | Convergence (routing),Robot learning,Bilinear form,Control theory,Computer science,Bootstrapping,Hebbian theory,Robot vision systems,Actuator,Bilinear interpolation | Conference |
Volume | Issue | ISSN |
2011 | 1 | 1050-4729 |
ISBN | Citations | PageRank |
978-1-61284-386-5 | 6 | 0.57 |
References | Authors | |
13 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Andrea Censi | 1 | 551 | 39.63 |
| Richard M. Murray | 2 | 12322 | 1223.70 |