论文标题
BADGR：一个自主的基于学习的导航系统
BADGR: An Autonomous Self-Supervised Learning-Based Navigation System
论文作者
论文摘要
移动机器人导航通常被视为几何问题，其中机器人的目标是感知环境的几何形状，以便将无碰撞路径计划针对所需的目标。但是，对于许多导航问题来说，纯粹的几何视图可能不足。例如，基于几何形状导航的机器人可能会避免使用高大的草地，因为它认为它是不可转化的，因此将无法实现其期望的目标。在这项工作中，我们使用一种了解从经验中的物理导航提供的方法来研究如何超越这些纯粹基于几何的方法。我们称之为Badgr的方法是一种基于端到端的基于学习的移动机器人导航系统，可以通过在实际环境中收集的自我监督的非货币数据培训，而无需任何模拟或人类监督。 Badgr可以在现实世界中的城市和越野环境中导航，并分散了几何障碍。它还可以结合地形偏好，概括为新的环境，并通过收集更多数据继续自主改善。视频，代码和其他补充材料可在我们的网站上找到https://sites.google.com/view/badgr
Mobile robot navigation is typically regarded as a geometric problem, in which the robot's objective is to perceive the geometry of the environment in order to plan collision-free paths towards a desired goal. However, a purely geometric view of the world can can be insufficient for many navigation problems. For example, a robot navigating based on geometry may avoid a field of tall grass because it believes it is untraversable, and will therefore fail to reach its desired goal. In this work, we investigate how to move beyond these purely geometric-based approaches using a method that learns about physical navigational affordances from experience. Our approach, which we call BADGR, is an end-to-end learning-based mobile robot navigation system that can be trained with self-supervised off-policy data gathered in real-world environments, without any simulation or human supervision. BADGR can navigate in real-world urban and off-road environments with geometrically distracting obstacles. It can also incorporate terrain preferences, generalize to novel environments, and continue to improve autonomously by gathering more data. Videos, code, and other supplemental material are available on our website https://sites.google.com/view/badgr
