We present IndoorSim-to-OutdoorReal (I2O), an end-to-end learned visual navigation approach, trained solely in simulated short-range indoor environments, and demonstrates zero-shot sim-to-real transfer to the outdoors for long-range navigation on the Spot robot. Our method uses zero real-world experience (indoor or outdoor), and requires the simulator to model no predominantly-outdoor phenomenon (sloped grounds, sidewalks, etc). The key to I2O transfer is in providing the robot with additional context of the environment (i.e., a satellite map, a rough sketch of a map by a human, etc.) to guide the robot's navigation in the real-world. The provided context-maps do not need to be accurate or complete -- real-world obstacles (e.g., trees, bushes, pedestrians, etc.) are not drawn on the map, and openings are not aligned with where they are in the real-world. Crucially, these inaccurate context-maps provide a hint to the robot about a route to take to the goal. We find that our method that leverages Context-Maps is able to successfully navigate hundreds of meters in novel environments, avoiding novel obstacles on its path, to a distant goal without a single collision or human intervention. In comparison, policies without the additional context fail completely. Lastly, we test the robustness of the Context-Map policy by adding varying degrees of noise to the map in simulation. We find that the Context-Map policy is surprisingly robust to noise in the provided context-map. In the presence of significantly inaccurate maps (corrupted with 50% noise, or entirely blank maps), the policy gracefully regresses to the behavior of a policy with no context. Videos are available at https://www.joannetruong.com/projects/i2o.html

翻译：我们提出室内模拟到户外现实（IndoorSim-to-OutdoorReal，I2O）方法，这是一种端到端学习的视觉导航方法，仅在模拟的短距离室内环境中训练，并在Spot机器人上展示了零样本从模拟到现实向户外长距离导航的迁移能力。我们的方法无需任何真实世界经验（室内或户外），且要求模拟器无需建模任何主要户外现象（如斜坡地面、人行道等）。I2O迁移的关键在于为机器人提供环境附加上下文信息（如卫星地图、人类绘制的粗略草图等），以引导其在真实世界中的导航。提供的上下文地图无需精确或完整——真实世界障碍物（如树木、灌木丛、行人等）未在地图上标出，且开口位置与现实世界未对齐。关键在于，这些不精确的上下文地图为机器人提供了前往目标路线的大致提示。我们发现，利用上下文地图的方法能在新颖环境中成功导航数百米，沿途避开新颖障碍物，无需任何碰撞或人工干预即可到达远方目标。相比之下，无附加上下文的策略则完全失败。最后，我们在仿真中通过向地图添加不同程度的噪声来测试上下文地图策略的鲁棒性。结果表明，上下文地图策略对所提供地图的噪声具有惊人的鲁棒性。在面对显著不准确的地图（掺杂50%噪声或完全空白地图）时，该策略能优雅地退化至无上下文策略的行为。视频见https://www.joannetruong.com/projects/i2o.html