Learning Efficient Policies for Vision-based Navigation
Authors: Maren Bennewitz.
In this talk, I will present a new approach to learning efficient
navigation policies using visual features for localization. Fast
movements of a mobile robot typically reduce the performance of
vision-based localization systems due to motion blur. In the presented
approach, the robot learns a policy on how to reach its destination
reliably and, at the same time, as fast as possible. Thereby, the
impact of motion blur on the observations is implicitly taken into
account and delays caused by localization errors are avoided. To
reduce the size of the resulting policy, which is desirable in the
context of memory-constrained systems, the learned policy is
compressed via a clustering approach. I will present experiments
demonstrating that the learned policy significantly outperforms any
policy that uses a constant velocity. Additional experiments show
that the compressed policy does not result in a loss of performance
compared to the originally learned policy.
Furthermore, I will shortly introduce a novel approach for learning a
landmark selection policy for navigation in unknown environments that
allows a robot to discard landmarks that are not valuable for its
current navigation task. This enables the robot to reduce the
computational burden and to carry out its task more efficiently by
maintaining only the important landmarks.