Learning-based methods are promising to plan robot motion without performing
extensive search, which is needed by many non-learning approaches. Recently,
Value Iteration Networks (VINs) received much interest since---in contrast to
standard CNN-based architectures---they learn goal-directed behaviors which
generalize well to unseen domains. However, VINs are restricted to small and
low-dimensional domains, limiting their applicability to real-world planning
problems.
To address this issue, we propose to extend VINs to representations with
multiple levels of abstraction. While the vicinity of the robot is represented
in sufficient detail, the representation gets spatially coarser with increasing
distance from the robot. The information loss caused by the decreasing
resolution is compensated by increasing the number of features representing a
cell. We show that our approach is capable of solving significantly larger 2D
grid world planning tasks than the original VIN implementation. In contrast to
a multiresolution coarse-to-fine VIN implementation which does not employ
additional descriptive features, our approach is capable of solving challenging
environments, which demonstrates that the proposed method learns to encode
useful information in the additional features. As an application for solving
real-world planning tasks, we successfully employ our method to plan
omnidirectional driving for a search-and-rescue robot in cluttered terrain