We present a target-driven navigation system to improve mapless visual navigation in indoor scenes. Our method takes a multi-view observation of a robot and a target image as inputs at each time step to provide a sequence of actions that move the robot to the target without relying on odometry or GPS at runtime. The system is learned by optimizing a combinational objective encompassing three key designs. First, we propose that an agent conceives the next observation before making an action decision. This is achieved by learning a variational generative module from expert demonstrations. We then propose predicting static collision in advance, as an auxiliary task to improve safety during navigation. Moreover, to alleviate the training data imbalance problem of termination action prediction, we also introduce a target checking module to differentiate from augmenting navigation policy with a termination action. The three proposed designs all contribute to the improved training data efficiency, static collision avoidance, and navigation generalization performance, resulting in a novel target-driven mapless navigation system. Through experiments on a TurtleBot, we provide evidence that our model can be integrated into a robotic system and navigate in the real world. Videos and models can be found in the supplementary material.

中文翻译：

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通过生成模仿学习在室内场景中实现目标驱动的视觉导航

我们提出了一种目标驱动的导航系统，以改善室内场景中的无地图视觉导航。我们的方法将机器人的多视图观察和目标图像作为每个时间步的输入，以提供一系列动作，将机器人移动到目标，而无需在运行时依赖里程计或 GPS。该系统是通过优化包含三个关键设计的组合目标来学习的。首先，我们建议代理在做出行动决定之前构思下一个观察结果。这是通过从专家演示中学习变分生成模块来实现的。然后我们建议提前预测静态碰撞，作为提高导航安全性的辅助任务。此外，为了缓解终止动作预测的训练数据不平衡问题，我们还引入了一个目标检查模块，以区别于具有终止操作的增强导航策略。这三个提议的设计都有助于提高训练数据效率、静态碰撞避免和导航泛化性能，从而形成一种新颖的目标驱动的无地图导航系统。通过在 TurtleBot 上的实验，我们提供了证据，证明我们的模型可以集成到机器人系统中并在现实世界中导航。视频和模型可以在补充材料中找到。我们提供证据证明我们的模型可以集成到机器人系统中并在现实世界中导航。视频和模型可以在补充材料中找到。我们提供证据表明我们的模型可以集成到机器人系统中并在现实世界中导航。视频和模型可以在补充材料中找到。