Although visual navigation has been extensively studied using deep reinforcement learning, online learning for real-world robots remains a challenging task. Recent work directly learned from offline dataset to achieve broader generalization in the real-world tasks, which, however, faces the out-of-distribution (OOD) issue and potential robot localization failures in a given map for unseen observation. This significantly drops the success rates and even induces collision. In this paper, we present a self-correcting visual navigation method, SCALE, that can autonomously prevent the robot from the OOD situations without human intervention. Specifically, we develop an image-goal conditioned offline reinforcement learning method based on implicit Q-learning (IQL). When facing OOD observation, our novel localization recovery method generates the potential future trajectories by learning from the navigation affordance, and estimates the future novelty via random network distillation (RND). A tailored cost function searches for the candidates with the least novelty that can lead the robot to the familiar places. We collect offline data and conduct evaluation experiments in three real-world urban scenarios. Experiment results show that SCALE outperforms the previous state-of-the-art methods for open-world navigation with a unique capability of localization recovery, significantly reducing the need for human intervention. Code is available at https://github.com/KubeEdge4Robotics/ScaleNav.

翻译：尽管深度强化学习已被广泛用于视觉导航研究，面向真实世界机器人的在线学习仍是一项具有挑战性的任务。近期工作尝试直接从离线数据集学习以实现更广泛泛化，但这类方法在面对未观测场景时存在分布外（OOD）问题及地图内潜在定位失败风险，导致成功率显著下降甚至引发碰撞。本文提出自校正视觉导航方法SCALE，可在无需人工干预情况下自主防止机器人陷入OOD情形。具体而言，我们基于隐式Q学习（IQL）开发了一种图像目标约束的离线强化学习方法。当遇到OOD观测时，本方法通过导航可处理性学习生成潜在未来轨迹，并利用随机网络蒸馏（RND）估计未来新奇性，由此构建的定制化代价函数可搜索出能引导机器人返回熟悉环境的最少新奇性候选路径。我们在三个真实城市场景中收集离线数据并开展评估实验，结果表明SCALE在开放世界导航任务中以独特的定位恢复能力超越了现有最优方法，显著减少对人机交互的需求。代码已开源：https://github.com/KubeEdge4Robotics/ScaleNav。