Fast Incremental Learning for Off-Road Robot Navigation
Robotics
2016-06-28 v1
Abstract
A promising approach to autonomous driving is machine learning. In such systems, training datasets are created that capture the sensory input to a vehicle as well as the desired response. A disadvantage of using a learned navigation system is that the learning process itself may require a huge number of training examples and a large amount of computing. To avoid the need to collect a large training set of driving examples, we describe a system that takes advantage of the huge number of training examples provided by ImageNet, but is able to adapt quickly using a small training set for the specific driving environment.
Cite
@article{arxiv.1606.08057,
title = {Fast Incremental Learning for Off-Road Robot Navigation},
author = {Artem Provodin and Liila Torabi and Beat Flepp and Yann LeCun and Michael Sergio and L. D. Jackel and Urs Muller and Jure Zbontar},
journal= {arXiv preprint arXiv:1606.08057},
year = {2016}
}