Efficient navigation using slow feature gradients

摘要

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 circumnavigate them by simply following the steepest gradient in slow feature space.