Performing agile navigation with four-legged robots is a challenging task due to the highly dynamic motions, contacts with various parts of the robot, and the limited field of view of the perception sensors. In this paper, we propose a fully-learned approach to train such robots and conquer scenarios that are reminiscent of parkour challenges. The method involves training advanced locomotion skills for several types of obstacles, such as walking, jumping, climbing, and crouching, and then using a high-level policy to select and control those skills across the terrain. Thanks to our hierarchical formulation, the navigation policy is aware of the capabilities of each skill, and it will adapt its behavior depending on the scenario at hand. Additionally, a perception module is trained to reconstruct obstacles from highly occluded and noisy sensory data and endows the pipeline with scene understanding. Compared to previous attempts, our method can plan a path for challenging scenarios without expert demonstration, offline computation, a priori knowledge of the environment, or taking contacts explicitly into account. While these modules are trained from simulated data only, our real-world experiments demonstrate successful transfer on hardware, where the robot navigates and crosses consecutive challenging obstacles with speeds of up to two meters per second. The supplementary video can be found on the project website: https://sites.google.com/leggedrobotics.com/agile-navigation

翻译：四足机器人的敏捷导航是一项具有挑战性的任务，原因在于其高度动态的运动、与机器人各部位的接触以及感知传感器有限的视野。本文提出了一种全学习方法，用于训练此类机器人并攻克类似跑酷挑战的场景。该方法包括为多种障碍类型训练高级运动技能，例如行走、跳跃、攀爬和俯身，然后使用高层策略在地形上选择并控制这些技能。得益于我们的分层架构，导航策略能够感知每项技能的能力，并根据具体场景调整其行为。此外，我们训练了一个感知模块，用以从高度遮挡和噪声的传感数据中重建障碍物，并为整个流程赋予场景理解能力。与以往尝试相比，我们的方法无需专家示范、离线计算、环境先验知识或显式考虑接触，即可为挑战性场景规划路径。尽管这些模块仅使用模拟数据进行训练，我们的真实世界实验证明了其在硬件上的成功迁移——机器人以高达每秒两米的速度导航并穿越连续挑战性障碍物。补充视频可见于项目网站：https://sites.google.com/leggedrobotics.com/agile-navigation