This work focuses on the problem of visual target navigation, which is very important for autonomous robots as it is closely related to high-level tasks. To find a special object in unknown environments, classical and learning-based approaches are fundamental components of navigation that have been investigated thoroughly in the past. However, due to the difficulty in the representation of complicated scenes and the learning of the navigation policy, previous methods are still not adequate, especially for large unknown scenes. Hence, we propose a novel framework for visual target navigation using the frontier semantic policy. In this proposed framework, the semantic map and the frontier map are built from the current observation of the environment. Using the features of the maps and object category, deep reinforcement learning enables to learn a frontier semantic policy which can be used to select a frontier cell as a long-term goal to explore the environment efficiently. Experiments on Gibson and Habitat-Matterport 3D (HM3D) demonstrate that the proposed framework significantly outperforms existing map-based methods in terms of success rate and efficiency. Ablation analysis also indicates that the proposed approach learns a more efficient exploration policy based on the frontiers. A demonstration is provided to verify the applicability of applying our model to real-world transfer. The supplementary video and code can be accessed via the following link: https://sites.google.com/view/fsevn.

翻译：本文聚焦于视觉目标导航问题，该问题对自主机器人至关重要，因为它与高层级任务密切相关。为在未知环境中寻找特定物体，经典方法与基于学习的方法作为导航的基本组成部分，在过去已被广泛研究。然而，由于复杂场景表征及导航策略学习的困难性，先前的方法仍不够充分，尤其是在大型未知场景中。因此，我们提出了一种新颖的视觉目标导航框架，采用前沿语义策略。在该框架中，语义地图和前沿地图根据环境的当前观测构建。利用地图特征与物体类别，深度强化学习能够学习一种前沿语义策略，该策略可用于选择前沿单元格作为长期目标，从而高效探索环境。在Gibson和Habitat-Matterport 3D（HM3D）上的实验表明，所提框架在成功率和效率上显著优于现有的基于地图的方法。消融分析也表明，所提方法基于前沿学习到了更高效的探索策略。我们提供了一个演示来验证模型在真实世界迁移中的适用性。补充视频和代码可通过以下链接访问：https://sites.google.com/view/fsevn。