Autonomous navigation consists in an agent being able to navigate without human intervention or supervision, it affects both high level planning and low level control. Navigation is at the crossroad of multiple disciplines, it combines notions of computer vision, robotics and control. This work aimed at creating, in a simulation, a navigation pipeline whose transfer to the real world could be done with as few efforts as possible. Given the limited time and the wide range of problematic to be tackled, absolute navigation performances while important was not the main objective. The emphasis was rather put on studying the sim2real gap which is one the major bottlenecks of modern robotics and autonomous navigation. To design the navigation pipeline four main challenges arise; environment, localization, navigation and planning. The iGibson simulator is picked for its photo-realistic textures and physics engine. A topological approach to tackle space representation was picked over metric approaches because they generalize better to new environments and are less sensitive to change of conditions. The navigation pipeline is decomposed as a localization module, a planning module and a local navigation module. These modules utilize three different networks, an image representation extractor, a passage detector and a local policy. The laters are trained on specifically tailored tasks with some associated datasets created for those specific tasks. Localization is the ability for the agent to localize itself against a specific space representation. It must be reliable, repeatable and robust to a wide variety of transformations. Localization is tackled as an image retrieval task using a deep neural network trained on an auxiliary task as a feature descriptor extractor. The local policy is trained with behavioral cloning from expert trajectories gathered with ROS navigation stack.

中文翻译：

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为本地化和导航任务学习从合成到真实的转换

自主导航在于代理能够在无需人工干预或监督的情况下进行导航，这会影响高层计划和底层控制。导航处于多个学科的十字路口，它结合了计算机视觉，机器人技术和控制等概念。这项工作旨在通过仿真创建一条导航管道，该管道可以通过尽可能少的努力完成向真实世界的转移。鉴于时间有限且需要解决的问题种类繁多，绝对重要的导航性能并不是主要目标。相反，重点是研究模拟现实差距，这是现代机器人技术和自主导航的主要瓶颈之一。设计导航管道出现了四个主要挑战。环境，本地化，导航和规划。选择iGibson模拟器是因为它具有逼真的纹理和物理引擎。选择了一种解决空间表示的拓扑方法，而不是采用度量方法，因为它们可以更好地推广到新环境中，并且对条件变化不那么敏感。导航管道被分解为定位模块，计划模块和本地导航模块。这些模块利用三个不同的网络，图像表示提取器，通过检测器和本地策略。后来者针对专门定制的任务进行了培训，并为这些特定任务创建了一些关联的数据集。本地化是代理针对特定空间表示对自己进行本地化的能力。对于各种各样的转换，它必须是可靠的，可重复的和鲁棒的。使用在辅助任务上训练的深度神经网络作为特征描述符提取器，将本地化作为图像检索任务解决。通过从ROS导航堆栈中收集的专家轨迹中的行为克隆来训练本地策略。