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arXiv · 2403.03269

Active Information Gathering for Long-Horizon Navigation Under Uncertainty by Learning the Value of Information

Raihan Islam Arnob·Gregory J. Stein
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Abstract

We address the task of long-horizon navigation in partially mapped environments for which active gathering of information about faraway unseen space is essential for good behavior. We present a novel planning strategy that, at training time, affords tractable computation of the value of information associated with revealing potentially informative regions of unseen space, data used to train a graph neural network to predict the goodness of temporally-extended exploratory actions. Our learning-augmented model-based planning approach predicts the expected value of information of revealing unseen space and is capable of using these predictions to actively seek information and so improve long-horizon navigation. Across two simulated office-like environments, our planner outperforms competitive learned and non-learned baseline navigation strategies, achieving improvements of up to 63.76% and 36.68%, demonstrating its capacity to actively seek performance-critical information.

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Raihan Islam Arnob, Gregory J. Stein. 2024-03-05. Active Information Gathering for Long-Horizon Navigation Under Uncertainty by Learning the Value of Information. https://doi.org/10.1109/iros58592.2024.10801868

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