Online learning for characterizing unknown environments in ground robotic vehicle models
Alec Koppel, Jonathan Fink, Garrett Warnell, Ethan Stump, Alejandro Ribeiro
Abstract
In pursuit of increasing the operational tempo of a ground robotics platform in unknown domains, we consider the problem of predicting the distribution of structural state-estimation error due to poorly-modeled platform dynamics as well as environmental effects. Such predictions are a critical component of any modern control approach that utilizes uncertainty information to provide robustness in control design. We use an online learning algorithm based on matrix factorization techniques to fit a statistical model of error that provides enough expressive power to enable prediction directly from motion control signals and low-level visual features. Moreover, we empirically demonstrate that this technique compares favorably to predictors that do not incorporate this information.
BibTeX
@inproceedings{iros2016_onlinelearningfo,
title = {Online learning for characterizing unknown environments in ground robotic vehicle models},
author = {Alec Koppel and Jonathan Fink and Garrett Warnell and Ethan Stump and Alejandro Ribeiro},
booktitle = {IROS 2016},
year = {2016}
}