1 research outputs found
Global Navigation Using Predictable and Slow Feature Analysis in Multiroom Environments, Path Planning and Other Control Tasks
Extended Predictable Feature Analysis (PFAx) [Richthofer and Wiskott, 2017]
is an extension of PFA [Richthofer and Wiskott, 2015] that allows generating a
goal-directed control signal of an agent whose dynamics has previously been
learned during a training phase in an unsupervised manner. PFAx hardly requires
assumptions or prior knowledge of the agent's sensor or control mechanics, or
of the environment. It selects features from a high-dimensional input by
intrinsic predictability and organizes them into a reasonably low-dimensional
model.
While PFA obtains a well predictable model, PFAx yields a model ideally
suited for manipulations with predictable outcome. This allows for
goal-directed manipulation of an agent and thus for local navigation, i.e. for
reaching states where intermediate actions can be chosen by a permanent descent
of distance to the goal. The approach is limited when it comes to global
navigation, e.g. involving obstacles or multiple rooms.
In this article, we extend theoretical results from [Sprekeler and Wiskott,
2008], enabling PFAx to perform stable global navigation. So far, the most
widely exploited characteristic of Slow Feature Analysis (SFA) was that
slowness yields invariances. We focus on another fundamental characteristics of
slow signals: They tend to yield monotonicity and one significant property of
monotonicity is that local optimization is sufficient to find a global optimum.
We present an SFA-based algorithm that structures an environment such that
navigation tasks hierarchically decompose into subgoals. Each of these can be
efficiently achieved by PFAx, yielding an overall global solution of the task.
The algorithm needs to explore and process an environment only once and can
then perform all sorts of navigation tasks efficiently. We support this
algorithm by mathematical theory and apply it to different problems