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

arXiv: https://arxiv.org/abs/2403.03269 · HTML: https://mlsanity.com/paper/2403.03269/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Mar 2024.

## Built on by (most cited first, 1 of 1)

- [Scout-Assisted Planning for Heterogeneous Robot Teams under Partially Known Environments](https://mlsanity.com/paper/2605.22693/) (2026)

## Abstract (arXiv)

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.