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Learning Efficient Policies for Vision-based Navigation

Cameras provide valuable information about the environment and are often used as sensor for localization to accomplish navigation tasks. However, fast movements of a mobile robot typically reduce the performance of vision-based localization systems due to motion blur. We developed a reinforcement learning approach to select appropriate velocity values for vision-based navigation. The learned policy minimizes the time to reach the destination and implicitly takes the impact of motion blur on observations into account. To reduce the size of the resulting policies, which is desirable in the context of memory-constrained systems, we compress the learned policy via a clustering approach. Extensive simulated and real-world experiments demonstrate that our learned policy significantly outperforms any policy that uses a constant velocity and more advanced heuristics.

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In our experiments, we used a Pioneer 2 robot with a top-mounted down-looking camera to observe the floor in front of the robot. In the experiment shown in the video below, the robot drives to its goal position using the learned policy. The current camera image with detected and matched landmarks as well as the robot's true (red) and the via UKF estimated (green) pose and the corresponding uncertainty are displayed. Depending on the distance and the angle to the goal as well as on the uncertainty in the belief about the robot's pose, the robot chooses appropriate values for the translational velocity to reach the destination as fast as possible.
We furthermore performed experiments with an outdoor robot and used a scenario in which the robot had to traverse several waypoints. The second part of the video below shows the robot driving to its goal using the learned policy. As can be seen, the robot reaches the goal fast and reliably.

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