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，因其与高级任务紧密相关。在未知环境中寻找特定物体时，经典方法与基于学习方法构成了导航的基础组件，并已在过去得到深入研究。然而，由于复杂场景表征与导航策略学习的困难，现有方法仍显不足，尤其在大型未知场景中。为此，我们提出一种基于边界语义策略的视觉目标导航新框架。在该框架中，语义地图与边界地图根据当前环境观测构建。通过利用地图特征与物体类别，深度强化学习能够学习一种边界语义策略，该策略可选取一个边界单元作为长期目标，以高效探索环境。在Gibson与Habitat-Matterport 3D（HM3D）上的实验表明，所提框架在成功率与效率上显著优于现有基于地图的方法。消融分析进一步指出，本方法基于边界学习到了更高效的探索策略。通过演示验证了模型应用于真实环境迁移的可行性。补充视频与代码可通过以下链接获取：https://sites.google.com/view/fsevn。