We present NavACL, a method of automatic curriculum learning tailored to the navigation task. NavACL is simple to train and efficiently selects relevant tasks using geometric features. In our experiments, deep reinforcement learning agents trained using NavACL in collision-free environments significantly outperform state-of-the-art agents trained with uniform sampling -- the current standard. Furthermore, our agents are able to navigate through unknown cluttered indoor environments to semantically-specified targets using only RGB images. Collision avoidance policies and frozen feature networks support transfer to unseen real-world environments, without any modification or retraining requirements. We evaluate our policies in simulation, and in the real world on a ground robot and a quadrotor drone. Videos of real-world results are available in the supplementary material

中文翻译：

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在真实环境中具有自动课程学习的具身视觉导航

我们提出了 NavACL，这是一种为导航任务量身定制的自动课程学习方法。NavACL 易于训练并使用几何特征高效地选择相关任务。在我们的实验中，在无碰撞环境中使用 NavACL 训练的深度强化学习代理明显优于使用统一采样（当前标准）训练的最先进代理。此外，我们的代理能够仅使用 RGB 图像在未知的杂乱室内环境中导航到语义指定的目标。碰撞避免策略和冻结特征网络支持转移到看不见的现实世界环境，无需任何修改或再培训要求。我们在模拟中以及在地面机器人和四旋翼无人机的现实世界中评估我们的策略。