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.

翻译：我们研究了部分地图环境下长时域导航任务，其中主动收集远距离未知空间信息对实现良好性能至关重要。本文提出一种新型规划策略，该策略在训练阶段可高效计算揭示潜在信息丰富未知区域的信息价值，并利用这些数据训练图神经网络，用于预测时间扩展探索动作的优良性。我们的学习增强型模型规划方法能预测揭示未知空间的期望信息价值，并利用这些预测主动搜索信息，从而提升长时域导航性能。在两个模拟办公环境中的实验表明，该规划器显著优于竞争性的学习与非学习基线导航策略，性能提升分别达63.76%和36.68%，充分展现了其主动搜索关键性能信息的能力。