@inproceedings {pub3373,
	title = {Efficient Navigation Using Slow Feature Gradients},
	author = {Benjamin Metka AND Mathias Franzius AND Ute Bauer-Wersing},
	year = {2017},
	month = {September},
	abstract = {A model of hierarchical Slow Feature Analysis
(SFA) enables a mobile robot to learn a spatial representation of
its environment directly from images captured during a random
walk. After the unsupervised learning phase a subset of the
resulting representations are orientation invariant and code for
the position of the robot. Hence, they change monotonically
over space even though the variation of the sensory signals
received from the environment might change drastically e.g.
during rotation on the spot. Furthermore, the property of
spatial smoothness allows us to infer a navigation direction by
taking the difference between the measurement at the current
location and a measurement at a target location. In our work
we investigated the use of slow feature representations, learned
for a specific environment, for the purpose of navigation. We
present a straightforward method for navigation using gradient
descent on the difference between two points specified in slow
feature space. Due to its slowness objective, the resulting slow
feature representations implicitly encode information about
static obstacles allowing a mobile robot to efficiently circum-
navigate them by simply following the steepest gradient in slow
feature space.
},
	publisher = {IEEE},
	booktitle = {International Conference on Intelligent Robots and Systems (IROS)}
}