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 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.

翻译：我们提出一个目标驱动导航系统,以改善室内场景的无地图视觉导航。 我们的方法是对机器人进行多视图观测,然后将目标作为每个时间步骤的投入,提供一系列行动,将机器人转移到目标,而不必依赖视光学或运行时全球定位系统。 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, 我们提议在采取行动之前, 由代理人来构思下一个观测。 这是通过学习专家演示的变异基因模块来实现的。 然后, 我们提议预先预测静态碰撞, 作为改善导航期间安全的辅助任务。 此外, 为了减轻终止行动预测中的数据不平衡问题, 我们还引入一个目标检查模块, 以区别于以终止行动来扩大导航政策。 三个拟议设计都有助于提高培训数据效率、 静态碰撞和导航通用性能, 从而形成一个新的目标驱动的无地图导航系统。 我们通过在TurtusBot上进行的实验, 提供了证据, 我们的模型可以融入机器人系统并在现实世界中导航。 视频和模型可以在辅助材料中找到。