Improving Reliable Navigation under Uncertainty with Non-Local Information Informed Predictions

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We focus on the task of goal-directed navigation in a partially-mapped environment, in which a robot is expected to reach an unseen goal in minimum expected time. To perform well, a robot must understand how parts of the environment the robot cannot currently see (i.e., non-locally available information) to inform where it should go next, a challenging problem for many existing planning strategies…

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