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：我们提出室内模拟至室外真实（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机器人上实现了从模拟到外场环境的零样本迁移，支持长距离导航。我们的方法无需任何真实经验（室内或室外），且要求模拟器不建模任何典型的室外现象（如斜坡地面、人行道等）。I2O迁移的关键在于为机器人提供额外的环境上下文（如卫星地图、人类手绘的粗略地图等），以引导机器人在真实世界中的导航。提供的上下文地图无需精确或完整——真实障碍物（如树木、灌木丛、行人等）未在地图上标注，且开口位置与现实世界不对齐。至关重要的是，这些不精确的上下文地图为机器人提供了通往目标路径的提示。我们发现，利用上下文地图的方法能够在陌生环境中成功导航数百米，沿途避开未知障碍物，无需任何碰撞或人工干预即可抵达远处目标。相比之下，缺乏额外上下文的策略完全失败。最后，我们通过在模拟环境中向地图添加不同程度的噪声，测试了上下文地图策略的鲁棒性。结果发现，上下文地图策略对所提供的上下文地图中的噪声具有惊人的鲁棒性。当地图显著不准确时（如掺杂50%噪声或完全空白），策略会优雅地退化为无上下文策略的行为。视频见https://www.joannetruong.com/projects/i2o.html