论文标题
花生：预测和导航到看不见的目标
PEANUT: Predicting and Navigating to Unseen Targets
论文作者
论文摘要
新颖环境中有效的对象导航（ObjectNAV）需要了解环境布局中的空间和语义规律。在这项工作中，我们提出了一种直接的方法，可以通过从不完整的语义图中预测未观察到的对象的位置来学习这些规律性。我们的方法与以前的基于预测的导航方法（例如Frontier潜在预测或以自我为中心地图的完成）不同，通过直接预测看不见的目标，同时从所有先前探索的领域利用全局上下文。我们的预测模型是轻巧的，可以使用相对少量的被动收集数据以监督方式进行训练。一旦受过训练，该模型就可以将其纳入模块化管道中，以进行ObjectNAV，而无需进行任何强化学习。我们验证方法对HM3D和MP3D ObjectNAV数据集的有效性。我们发现，尽管没有使用任何其他数据进行培训，但它在两个数据集上都可以实现最新的。
Efficient ObjectGoal navigation (ObjectNav) in novel environments requires an understanding of the spatial and semantic regularities in environment layouts. In this work, we present a straightforward method for learning these regularities by predicting the locations of unobserved objects from incomplete semantic maps. Our method differs from previous prediction-based navigation methods, such as frontier potential prediction or egocentric map completion, by directly predicting unseen targets while leveraging the global context from all previously explored areas. Our prediction model is lightweight and can be trained in a supervised manner using a relatively small amount of passively collected data. Once trained, the model can be incorporated into a modular pipeline for ObjectNav without the need for any reinforcement learning. We validate the effectiveness of our method on the HM3D and MP3D ObjectNav datasets. We find that it achieves the state-of-the-art on both datasets, despite not using any additional data for training.
